diff --git a/dissertation/Other.bib b/dissertation/Other.bib new file mode 100644 index 0000000..a727661 --- /dev/null +++ b/dissertation/Other.bib @@ -0,0 +1,77 @@ + +@book{hastie_elements_2009, + title = {The Elements of Statistical Learning}, + volume = {1}, + isbn = {978-0-387-84857-0}, + url = {http://www.springerlink.com/index/10.1007/b94608}, + abstract = {During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting-the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-{PLUS} and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including {CART}, {MARS}, and projection pursuit. {FROM} {THE} {REVIEWS}: {TECHNOMETRICS} "This is a vast and complex book. Generally, it concentrates on explaining why and how the methods work, rather than how to use them. Examples and especially the visualizations are principle features...As a source for the methods of statistical learning...it will probably be a long time before there is a competitor to this book."}, + pagetotal = {1–694}, + author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome}, + date = {2009}, + doi = {10.1007/b94608}, + pmid = {12377617}, + file = {Attachment:/home/dimitri/Zotero/storage/AVAIRYAL/Hastie, Tibshirani, Friedman - Unknown - Springer Series in Statistics The Elements of Statistical Learning The Elements of Statistical.pdf:application/pdf} +} + +@incollection{karp_reducibility_2010, + title = {Reducibility among combinatorial problems}, + isbn = {978-3-540-68274-5}, + url = {http://www.springerlink.com/index/10.1007/978-1-4684-2001-2_9%5Cnpapers3://publication/doi/10.1007/978-1-4684-2001-2_9}, + abstract = {A large class of computational problems involve the determination of properties of graphs, digraphs, integers, arrays of integers, finite families of finite sets, boolean formulas and elements of other countable domains. Through simple encodings from such domains into the set of words over a finite alphabet these problems can be converted into language recognition problems, and we can inquire into their computational complexity. It is reasonable to consider such a problem satisfactorily solved when an algorithm for its solution is found which terminates within a number of steps bounded by a polynomial in the length of the input. We show that a large number of classic unsolved problems of covering, matching, packing, routing, assignment and sequencing are equivalent, in the sense that either each of them possesses a polynomial-bounded algorithm or none of them does.}, + pages = {219--241}, + number = {Chapter 9}, + booktitle = {50 Years of Integer Programming 1958-2008: From the Early Years to the State-of-the-Art}, + author = {Karp, Richard M.}, + date = {2010}, + doi = {10.1007/978-3-540-68279-0_8}, + pmid = {15890271}, + file = {Attachment:/home/dimitri/Zotero/storage/4IAHTPB5/Karp - 1972 - Reducibility among Combinatorial Problems BT - (null).pdf:application/pdf} +} + +@article{tomita_worst-case_2006, + title = {The worst-case time complexity for generating all maximal cliques and computational experiments}, + volume = {363}, + issn = {0304-3975}, + url = {http://www.sciencedirect.com/science/article/pii/S0304397506003586}, + doi = {10.1016/j.tcs.2006.06.015}, + series = {Computing and Combinatorics}, + abstract = {We present a depth-first search algorithm for generating all maximal cliques of an undirected graph, in which pruning methods are employed as in the Bron–Kerbosch algorithm. All the maximal cliques generated are output in a tree-like form. Subsequently, we prove that its worst-case time complexity is O(3n/3) for an n-vertex graph. This is optimal as a function of n, since there exist up to 3n/3 maximal cliques in an n-vertex graph. The algorithm is also demonstrated to run very fast in practice by computational experiments.}, + pages = {28--42}, + number = {1}, + journaltitle = {Theoretical Computer Science}, + shortjournal = {Theoretical Computer Science}, + author = {Tomita, Etsuji and Tanaka, Akira and Takahashi, Haruhisa}, + urldate = {2018-07-31}, + date = {2006-10-25}, + keywords = {Computational experiments, Enumeration, Maximal cliques, Worst-case time complexity}, + file = {ScienceDirect Full Text PDF:/home/dimitri/Zotero/storage/QDLTAXHX/Tomita et al. - 2006 - The worst-case time complexity for generating all .pdf:application/pdf;ScienceDirect Snapshot:/home/dimitri/Zotero/storage/TCJ8J7MV/S0304397506003586.html:text/html} +} + +@article{pedregosa_scikit-learn:_2011, + title = {Scikit-learn: Machine Learning in Python}, + volume = {12}, + issn = {1533-7928}, + url = {http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html}, + shorttitle = {Scikit-learn}, + abstract = {Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and {API} consistency. It has minimal dependencies and is distributed under the simplified {BSD} license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.}, + pages = {2825−2830}, + journaltitle = {Journal of Machine Learning Research}, + author = {Pedregosa, Fabian and Varoquaux, Gaël and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Édouard}, + urldate = {2018-09-02}, + date = {2011-10}, + file = {Fulltext PDF:/home/dimitri/Zotero/storage/6SAE9PPD/Pedregosa et al. - 2011 - Scikit-learn Machine Learning in Python.pdf:application/pdf} +} + +@misc{sejdinovic_advanced_2018, + title = {Advanced Topics in Statistical Machine Learning}, + url = {http://www.stats.ox.ac.uk/%7Esejdinov/atsml/}, + author = {Sejdinovic, Dino}, + date = {2018-02-10} +} + +@misc{golse_mat321_2015, + title = {{MAT}321 Analyse réelle}, + publisher = {École polytechnique}, + author = {Golse, François and Laszlo, Yves and Pacard, Frank and Viterbo, Claude}, + date = {2015} +} \ No newline at end of file diff --git a/dissertation/TDA.bib b/dissertation/TDA.bib index 702b247..a050780 100644 --- a/dissertation/TDA.bib +++ b/dissertation/TDA.bib @@ -738,18 +738,6 @@ novel application of the discriminatory power of {PIs}.}, file = {Fulltext PDF:/home/dimitri/Zotero/storage/EUWNMLQF/Adams et al. - 2017 - Persistence Images A Stable Vector Representation.pdf:application/pdf} } -@article{bubenik_statistical_2015-1, - title = {Statistical Topological Data Analysis using Persistence Landscapes}, - volume = {16}, - url = {http://www.jmlr.org/papers/v16/bubenik15a.html}, - pages = {77--102}, - journaltitle = {Journal of Machine Learning Research}, - author = {Bubenik, Peter}, - urldate = {2018-06-12}, - date = {2015}, - file = {Full Text PDF:/home/dimitri/Zotero/storage/CJW9F5XG/Bubenik - 2015 - Statistical Topological Data Analysis using Persis.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/E2DN26NP/bubenik15a.html:text/html} -} - @article{kalisnik_tropical_2018, title = {Tropical Coordinates on the Space of Persistence Barcodes}, issn = {1615-3375, 1615-3383}, @@ -873,4 +861,187 @@ novel application of the discriminatory power of {PIs}.}, urldate = {2018-07-31}, date = {2010}, keywords = {computational topology, simplicial set, vietoris-rips complex, witness complex} +} + +@inproceedings{zeppelzauer_topological_2016, + title = {Topological Descriptors for 3D Surface Analysis}, + isbn = {978-3-319-39440-4 978-3-319-39441-1}, + url = {https://link.springer.com/chapter/10.1007/978-3-319-39441-1_8}, + doi = {10.1007/978-3-319-39441-1_8}, + series = {Lecture Notes in Computer Science}, + abstract = {We investigate topological descriptors for 3D surface analysis, i.e. the classification of surfaces according to their geometric fine structure. On a dataset of high-resolution 3D surface reconstructions we compute persistence diagrams for a 2D cubical filtration. In the next step we investigate different topological descriptors and measure their ability to discriminate structurally different 3D surface patches. We evaluate their sensitivity to different parameters and compare the performance of the resulting topological descriptors to alternative (non-topological) descriptors. We present a comprehensive evaluation that shows that topological descriptors are (i) robust, (ii) yield state-of-the-art performance for the task of 3D surface analysis and (iii) improve classification performance when combined with non-topological descriptors.}, + eventtitle = {International Workshop on Computational Topology in Image Context}, + pages = {77--87}, + booktitle = {Computational Topology in Image Context}, + publisher = {Springer, Cham}, + author = {Zeppelzauer, Matthias and Zieliński, Bartosz and Juda, Mateusz and Seidl, Markus}, + urldate = {2018-08-16}, + date = {2016-06-15}, + langid = {english}, + file = {Snapshot:/home/dimitri/Zotero/storage/JH8QTE5R/978-3-319-39441-1_8.html:text/html} +} + +@article{muandet_kernel_2017, + title = {Kernel Mean Embedding of Distributions: A Review and Beyond}, + volume = {10}, + issn = {1935-8237, 1935-8245}, + url = {https://www.nowpublishers.com/article/Details/MAL-060}, + doi = {10.1561/2200000060}, + shorttitle = {Kernel Mean Embedding of Distributions}, + abstract = {Kernel Mean Embedding of Distributions: A Review and Beyond}, + pages = {1--141}, + number = {1}, + journaltitle = {Foundations and Trends® in Machine Learning}, + shortjournal = {{MAL}}, + author = {Muandet, Krikamol and Fukumizu, Kenji and Sriperumbudur, Bharath and Schölkopf, Bernhard}, + urldate = {2018-08-30}, + date = {2017-06-28}, + file = {Full Text PDF:/home/dimitri/Zotero/storage/87JN65NM/Muandet et al. - 2017 - Kernel Mean Embedding of Distributions A Review a.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/7DS27M8D/MAL-060.html:text/html} +} + +@book{berlinet_reproducing_2011, + title = {Reproducing Kernel Hilbert Spaces in Probability and Statistics}, + isbn = {978-1-4419-9096-9}, + abstract = {The reproducing kernel Hilbert space construction is a bijection or transform theory which associates a positive definite kernel (gaussian processes) with a Hilbert space offunctions. Like all transform theories (think Fourier), problems in one space may become transparent in the other, and optimal solutions in one space are often usefully optimal in the other. The theory was born in complex function theory, abstracted and then accidently injected into Statistics; Manny Parzen as a graduate student at Berkeley was given a strip of paper containing his qualifying exam problem- It read "reproducing kernel Hilbert space"- In the 1950's this was a truly obscure topic. Parzen tracked it down and internalized the subject. Soon after, he applied it to problems with the following fla vor: consider estimating the mean functions of a gaussian process. The mean functions which cannot be distinguished with probability one are precisely the functions in the Hilbert space associated to the covariance kernel of the processes. Parzen's own lively account of his work on re producing kernels is charmingly told in his interview with H. Joseph Newton in Statistical Science, 17, 2002, p. 364-366. Parzen moved to Stanford and his infectious enthusiasm caught Jerry Sacks, Don Ylvisaker and Grace Wahba among others. Sacks and Ylvis aker applied the ideas to design problems such as the following. Sup pose ({XdO}}, + pagetotal = {369}, + publisher = {Springer Science \& Business Media}, + author = {Berlinet, Alain and Thomas-Agnan, Christine}, + date = {2011-06-28}, + langid = {english}, + note = {Google-Books-{ID}: {bX}3TBwAAQBAJ}, + keywords = {Business \& Economics / Economics / General, Business \& Economics / Economics / Theory, Business \& Economics / General, Business \& Economics / Statistics, Mathematics / Probability \& Statistics / General} +} + +@thesis{price-wright_topological_2015, + title = {A Topological Approach to Temporal Networks}, + abstract = {“Temporal networks” are a mathematical tool to represent systems that change +over time. Research on temporal networks is very active, and limited theoreti- +cal work has been done to study them. One approach to is to construct a series +of static subgraphs called snapshots. Existing techniques attempt to find the +temporal structure of a network to inform its partitioning into snapshots. An +important goal of such methods is to uncover meaningful temporal structure +that corresponds to actual features of the underlying system. +We investigate existing methods used to partition temporal networks based +on di↵erent ways of identifying temporal structure. Such methods have never +previously been compared directly to each other, so we examine and evaluate +their performance side-by-side on a suite of random-graph ensembles. We +show that without prior knowledge about a network’s temporal structure, +these existing methods have limitations producing meaningful partitions. +To tackle the problem of finding temporal structure in a network, we ap- +ply methods from computational topology. Such methods have begun to be +employed in the study of static networks and provide a summary of global +features in data sets. We use them here to track the topology of a network +over time and distinguish important temporal features from trivial ones. We +define two types of topological spaces derived from temporal networks and use +persistent homology to generate a temporal profile for a network. We then +present di↵erent ways to use this to understand a network’s temporal struc- +ture with limited prior knowledge. We show that the methods we apply from +computational topology can distinguish temporal distributions and provide a +high-level summary of temporal structure. These combined can be used to +inform a meaningful network partitioning and a deeper understanding of a +temporal network itself.}, + institution = {University of Oxford}, + type = {{MSc} dissertation in Mathematics and Foundations of Computer Science}, + author = {Price-Wright, Erin}, + date = {2015}, + file = {Price-Wrigt - 2015 - A Topological Approach to Temporal Networks.pdf:/home/dimitri/Zotero/storage/6YI5RC6K/Price-Wrigt - 2015 - A Topological Approach to Temporal Networks.pdf:application/pdf} +} + +@inproceedings{edelsbrunner_topological_2000, + title = {Topological persistence and simplification}, + doi = {10.1109/SFCS.2000.892133}, + abstract = {We formalize a notion of topological simplification within the framework of a filtration, which is the history of a growing complex. We classify a topological change that happens during growth as either a feature or noise, depending on its life-time or persistence within the filtration. We give fast algorithms for completing persistence and experimental evidence for their speed and utility.}, + eventtitle = {Proceedings 41st Annual Symposium on Foundations of Computer Science}, + pages = {454--463}, + booktitle = {Proceedings 41st Annual Symposium on Foundations of Computer Science}, + author = {Edelsbrunner, H. and Letscher, D. and Zomorodian, A.}, + date = {2000-11}, + keywords = {Topology, History, computational topology, algorithm theory, alpha shapes, computational geometry, Computational geometry, Computer graphics, Computer science, Density functional theory, fast algorithms, filtration, Filtration, growing complex, homology groups, Mathematics, Noise shaping, Shape, topological change, topological persistence, topological simplification, topology}, + file = {IEEE Xplore Abstract Record:/home/dimitri/Zotero/storage/5LPIWG5Z/892133.html:text/html} +} + +@article{morozov_persistence_2005, + title = {Persistence algorithm takes cubic time in worst case}, + abstract = {Given a sequence of N simplices, we consider the sequence of sets Ki consisting of the first i simplices, for 1 ≤ i ≤ N. We call the sequence of Ki a filtration if all the Ki are simplicial complexes. In this note, we describe a filtration of a simplicial complex of N simplices on which the algorithm Pair-Simplices of Edelsbrunner, Letscher and Zomorodian [1] performs Ω(N 3) operations. The existence of this filtration should be contrasted to the experimentally observed only slightly super-linear running time for filtrations that arise from applications. We describe the space as well as the ordering on the simplices. Let n = ⌊(N + 29)/7⌋, v = ⌊(n − 1)/2⌋, and note that both n and v are in Ω(N). In our filtration, all vertices appear before all edges in the filtration, and all edges appear before all triangles. The indices that we assign to the simplices will be within their respective classes (e.g., edge labeled n will appear before the triangle labeled 1). Some edges will receive a negative index, which is done for simplicity to indicate that they appear before the edges with positive labels (see Figure 2). Figure 1 illustrates the construction of our space as well as the assignment of indices to the simplices. Starting with triangle {ABC}, we add v vertices inside the triangle in the following manner: we place the first vertex V1 near the middle of edge {AB}, the second vertex V2 near the middle of}, + journaltitle = {{BioGeometry} News, Dept. Comput. Sci., Duke Univ}, + author = {Morozov, Dmitriy}, + date = {2005}, + file = {Citeseer - Full Text PDF:/home/dimitri/Zotero/storage/I6HKHZQ5/Morozov - 2005 - Persistence algorithm takes cubic time in worst ca.pdf:application/pdf;Citeseer - Snapshot:/home/dimitri/Zotero/storage/WN8ZVZH9/summary.html:text/html} +} + +@article{de_silva_persistent_2011, + title = {Persistent Cohomology and Circular Coordinates}, + volume = {45}, + issn = {1432-0444}, + url = {https://doi.org/10.1007/s00454-011-9344-x}, + doi = {10.1007/s00454-011-9344-x}, + abstract = {Nonlinear dimensionality reduction ({NLDR}) algorithms such as Isomap, {LLE}, and Laplacian Eigenmaps address the problem of representing high-dimensional nonlinear data in terms of low-dimensional coordinates which represent the intrinsic structure of the data. This paradigm incorporates the assumption that real-valued coordinates provide a rich enough class of functions to represent the data faithfully and efficiently. On the other hand, there are simple structures which challenge this assumption: the circle, for example, is one-dimensional, but its faithful representation requires two real coordinates. In this work, we present a strategy for constructing circle-valued functions on a statistical data set. We develop a machinery of persistent cohomology to identify candidates for significant circle-structures in the data, and we use harmonic smoothing and integration to obtain the circle-valued coordinate functions themselves. We suggest that this enriched class of coordinate functions permits a precise {NLDR} analysis of a broader range of realistic data sets.}, + pages = {737--759}, + number = {4}, + journaltitle = {Discrete \& Computational Geometry}, + shortjournal = {Discrete Comput Geom}, + author = {de Silva, Vin and Morozov, Dmitriy and Vejdemo-Johansson, Mikael}, + urldate = {2018-09-05}, + date = {2011-06-01}, + langid = {english}, + keywords = {Persistent homology, Computational topology, Dimensionality reduction, Persistent cohomology}, + file = {Springer Full Text PDF:/home/dimitri/Zotero/storage/EX9L3F7F/de Silva et al. - 2011 - Persistent Cohomology and Circular Coordinates.pdf:application/pdf} +} + +@article{de_silva_dualities_2011, + title = {Dualities in persistent (co)homology}, + volume = {27}, + issn = {0266-5611, 1361-6420}, + url = {http://stacks.iop.org/0266-5611/27/i=12/a=124003?key=crossref.1f4b24ef80c9b1fc789ecdc6221097de}, + doi = {10.1088/0266-5611/27/12/124003}, + pages = {124003}, + number = {12}, + journaltitle = {Inverse Problems}, + author = {de Silva, Vin and Morozov, Dmitriy and Vejdemo-Johansson, Mikael}, + urldate = {2018-09-05}, + date = {2011-12-01} +} + +@incollection{mcgeoch_distributed_2014, + location = {Philadelphia, {PA}}, + title = {Distributed Computation of Persistent Homology}, + isbn = {978-1-61197-319-8}, + url = {http://epubs.siam.org/doi/abs/10.1137/1.9781611973198.4}, + pages = {31--38}, + booktitle = {2014 Proceedings of the Sixteenth Workshop on Algorithm Engineering and Experiments ({ALENEX})}, + publisher = {Society for Industrial and Applied Mathematics}, + author = {Bauer, Ulrich and Kerber, Michael and Reininghaus, Jan}, + editor = {{McGeoch}, Catherine C. and Meyer, Ulrich}, + urldate = {2018-09-05}, + date = {2014-05}, + langid = {english}, + doi = {10.1137/1.9781611973198.4} +} + +@article{carlsson_zigzag_2008, + title = {Zigzag Persistence}, + url = {http://arxiv.org/abs/0812.0197}, + abstract = {We describe a new methodology for studying persistence of topological features across a family of spaces or point-cloud data sets, called zigzag persistence. Building on classical results about quiver representations, zigzag persistence generalises the highly successful theory of persistent homology and addresses several situations which are not covered by that theory. In this paper we develop theoretical and algorithmic foundations with a view towards applications in topological statistics.}, + journaltitle = {{arXiv}:0812.0197 [cs]}, + author = {Carlsson, Gunnar and de Silva, Vin}, + urldate = {2018-09-08}, + date = {2008-11-30}, + eprinttype = {arxiv}, + eprint = {0812.0197}, + keywords = {Computer Science - Computational Geometry, I.3.5}, + file = {arXiv\:0812.0197 PDF:/home/dimitri/Zotero/storage/PKSM89FF/Carlsson and de Silva - 2008 - Zigzag Persistence.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/QF37EI5F/0812.html:text/html} +} + +@article{maria_computing_2016, + title = {Computing Zigzag Persistent Cohomology}, + url = {http://arxiv.org/abs/1608.06039}, + abstract = {Zigzag persistent homology is a powerful generalisation of persistent homology that allows one not only to compute persistence diagrams with less noise and using less memory, but also to use persistence in new fields of application. However, due to the increase in complexity of the algebraic treatment of the theory, most algorithmic results in the field have remained of theoretical nature. This article describes an efficient algorithm to compute zigzag persistence, emphasising on its practical interest. The algorithm is a zigzag persistent cohomology algorithm, based on the dualisation of reflections and transpositions transformations within the zigzag sequence. We provide an extensive experimental study of the algorithm. We study the algorithm along two directions. First, we compare its performance with zigzag persistent homology algorithm and show the interest of cohomology in zigzag persistence. Second, we illustrate the interest of zigzag persistence in topological data analysis by comparing it to state of the art methods in the field, specifically optimised algorithm for standard persistent homology and sparse filtrations. We compare the memory and time complexities of the different algorithms, as well as the quality of the output persistence diagrams.}, + journaltitle = {{arXiv}:1608.06039 [cs]}, + author = {Maria, Clément and Oudot, Steve}, + urldate = {2018-09-08}, + date = {2016-08-21}, + eprinttype = {arxiv}, + eprint = {1608.06039}, + keywords = {Computer Science - Computational Geometry}, + file = {arXiv\:1608.06039 PDF:/home/dimitri/Zotero/storage/LJBHWTMY/Maria and Oudot - 2016 - Computing Zigzag Persistent Cohomology.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/TCKJGZET/1608.html:text/html} } \ No newline at end of file diff --git a/dissertation/clustering.py b/dissertation/clustering.py new file mode 100644 index 0000000..f7c0195 --- /dev/null +++ b/dissertation/clustering.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 + +import numpy as np + +from sklearn.cluster import AgglomerativeClustering +from sklearn.svm import OneClassSVM + +import dill + +import matplotlib +matplotlib.use("PDF") +import matplotlib.pyplot as plt +plt.style.use("fivethirtyeight") +plt.rcParams["figure.figsize"] = (10, 6) + + +N_CLUSTERS = 10 + +GENERATIVE = True +SOCIOPATTERNS = True + +if __name__=="__main__": + if GENERATIVE: + print("==== Generative model ====") + zz_dgms = dill.load(open("generative/zz_dgms.dill", "rb")) + wrcf_dgms = dill.load(open("generative/wrcf_dgms.dill", "rb")) + zz_gram1 = dill.load(open("generative/zz_gram1.dill", "rb")) + wrcf_gram1 = dill.load(open("generative/wrcf_gram1.dill", "rb")) + zz_distmat = dill.load(open("generative/zz_distmat.dill", "rb")) + wrcf_distmat = dill.load(open("generative/wrcf_distmat.dill", "rb")) + + print("Zigzag + kernel") + clf = AgglomerativeClustering( + n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average') + clf.fit(zz_gram1) + fig, ax = plt.subplots() + ax.step(range(len(clf.labels_)), clf.labels_, where='post') + ax.set_xlabel("Subnetwork") + ax.set_ylabel("Cluster") + fig.savefig("fig/gen_zz_k.pdf", transparent=True, + pad_inches=0.3, bbox_inches="tight") + + print("WRCF + kernel") + clf = AgglomerativeClustering( + n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average') + clf.fit(wrcf_gram1) + fig, ax = plt.subplots() + ax.step(range(len(clf.labels_)), clf.labels_, where='post') + ax.set_xlabel("Subnetwork") + ax.set_ylabel("Cluster") + fig.savefig("fig/gen_wrcf_k.pdf", transparent=True, + pad_inches=0.3, bbox_inches="tight") + + print("Zigzag + bottleneck") + clf = AgglomerativeClustering( + n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average') + clf.fit(zz_distmat) + fig, ax = plt.subplots() + ax.step(range(len(clf.labels_)), clf.labels_, where='post') + ax.set_xlabel("Subnetwork") + ax.set_ylabel("Cluster") + fig.savefig("fig/gen_zz_b.pdf", transparent=True, + pad_inches=0.3, bbox_inches="tight") + + print("WRCF + bottleneck") + clf = AgglomerativeClustering( + n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average') + clf.fit(wrcf_distmat) + fig, ax = plt.subplots() + ax.step(range(len(clf.labels_)), clf.labels_, where='post') + ax.set_xlabel("Subnetwork") + ax.set_ylabel("Cluster") + fig.savefig("fig/gen_wrcf_b.pdf", transparent=True, + pad_inches=0.3, bbox_inches="tight") + + if SOCIOPATTERNS: + print("==== SocioPatterns dataset ====") + zz_dgms = dill.load(open("sociopatterns/zz_dgms.dill", "rb")) + wrcf_dgms = dill.load(open("sociopatterns/wrcf_dgms.dill", "rb")) + zz_gram1 = dill.load(open("sociopatterns/zz_gram1.dill", "rb")) + wrcf_gram1 = dill.load(open("sociopatterns/wrcf_gram1.dill", "rb")) + zz_distmat = dill.load(open("sociopatterns/zz_distmat.dill", "rb")) + wrcf_distmat = dill.load(open("sociopatterns/wrcf_distmat.dill", "rb")) + + print("Zigzag + kernel") + clf = AgglomerativeClustering(n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average') + clf.fit(zz_gram1) + fig, ax = plt.subplots() + ax.step(range(len(clf.labels_)), clf.labels_, where='post') + ax.set_xlabel("Subnetwork") + ax.set_ylabel("Cluster") + fig.savefig("fig/sp_zz_k.pdf", transparent=True, pad_inches=0.3, bbox_inches="tight") + + print("WRCF + kernel") + clf = AgglomerativeClustering(n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average') + clf.fit(wrcf_gram1) + fig, ax = plt.subplots() + ax.step(range(len(clf.labels_)), clf.labels_, where='post') + ax.set_xlabel("Subnetwork") + ax.set_ylabel("Cluster") + fig.savefig("fig/sp_wrcf_k.pdf", transparent=True, pad_inches=0.3, bbox_inches="tight") + + print("Zigzag + bottleneck") + clf = AgglomerativeClustering(n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average') + clf.fit(zz_distmat) + fig, ax = plt.subplots() + ax.step(range(len(clf.labels_)), clf.labels_, where='post') + ax.set_xlabel("Subnetwork") + ax.set_ylabel("Cluster") + fig.savefig("fig/sp_zz_b.pdf", transparent=True, pad_inches=0.3, bbox_inches="tight") + + print("WRCF + bottleneck") + clf = AgglomerativeClustering(n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average') + clf.fit(wrcf_distmat) + fig, ax = plt.subplots() + ax.step(range(len(clf.labels_)), clf.labels_, where='post') + ax.set_xlabel("Subnetwork") + ax.set_ylabel("Cluster") + fig.savefig("fig/sp_wrcf_b.pdf", transparent=True, pad_inches=0.3, bbox_inches="tight") diff --git a/dissertation/dissertation.pdf b/dissertation/dissertation.pdf index f66d6f7..1103fcd 100644 Binary files a/dissertation/dissertation.pdf and b/dissertation/dissertation.pdf differ diff --git a/dissertation/dissertation.tex b/dissertation/dissertation.tex index d74499b..5b4e60e 100644 --- a/dissertation/dissertation.tex +++ b/dissertation/dissertation.tex @@ -2,9 +2,6 @@ \input{preamble} -\usepackage[firstpage]{draftwatermark} - - \begin{document} \pagestyle{plain} @@ -24,33 +21,32 @@ %% Inkscape L-system %% [C]++[C]++[C]++[C]++[C] %% B=DA++EA----CA[-DA----BA]++;C=+DA--EA[---BA--CA]+;D=-BA++CA[+++DA++EA]-;E=--DA++++BA[+EA++++CA]--CA;A= - \begin{tikzpicture} - \pgfdeclarelindenmayersystem{Penrose}{ - \symbol{M}{\pgflsystemdrawforward} - \symbol{N}{\pgflsystemdrawforward} - \symbol{O}{\pgflsystemdrawforward} - \symbol{P}{\pgflsystemdrawforward} - \symbol{A}{\pgflsystemdrawforward} - \symbol{+}{\pgflsystemturnright} - \symbol{-}{\pgflsystemturnleft} - \rule{M->OA++PA----NA[-OA----MA]++} - \rule{N->+OA--PA[---MA--NA]+} - \rule{O->-MA++NA[+++OA++PA]-} - \rule{P->--OA++++MA[+PA++++NA]--NA} - \rule{A->} - } - \draw[lindenmayer system={Penrose, axiom=[N]++[N]++[N]++[N]++[N], - order=2, angle=36, step=4pt}] - lindenmayer system; - \end{tikzpicture} + % \begin{tikzpicture} + % \pgfdeclarelindenmayersystem{Penrose}{ + % \symbol{M}{\pgflsystemdrawforward} + % \symbol{N}{\pgflsystemdrawforward} + % \symbol{O}{\pgflsystemdrawforward} + % \symbol{P}{\pgflsystemdrawforward} + % \symbol{A}{\pgflsystemdrawforward} + % \symbol{+}{\pgflsystemturnright} + % \symbol{-}{\pgflsystemturnleft} + % \rule{M->OA++PA----NA[-OA----MA]++} + % \rule{N->+OA--PA[---MA--NA]+} + % \rule{O->-MA++NA[+++OA++PA]-} + % \rule{P->--OA++++MA[+PA++++NA]--NA} + % \rule{A->} + % } + % \draw[lindenmayer system={Penrose, axiom=[N]++[N]++[N]++[N]++[N], + % order=2, angle=36, step=4pt}] + % lindenmayer system; + % \end{tikzpicture} % \vspace{2.2cm} \vfill - \large{A dissertation submitted in partial fulfilment of the degree of\\ - Master of Science in Applied Statistics}\\ + \large{A dissertation submitted in partial fulfilment of the requirements for the degree of\\ + Master of Science in Statistical Science}\\ \vspace{.5cm} \large{\emph{Department of Statistics,\\ 24--29 St Giles, Oxford, OX1 3LB}}\\ - \vspace{1cm} - \large{\thedate} + \vspace{1cm} \large{\thedate} \end{center} \end{titlingpage} @@ -63,33 +59,89 @@ \cleardoublepage% -\chapter*{Declaration of authorship} - -\emph{This my own work (except where otherwise indicated).}\\[2cm] - -\begin{center} - Date \hspace{.5\linewidth} Signature -\end{center} - - -\cleardoublepage% - +\vspace*{3cm} \begin{abstract} - Abstract here + Temporal networks are a mathematical model to represent interactions + evolving over time. As such, they have a multitude of applications, + from biology to physics to social networks. The study of dynamics on + networks is an emerging field, with many challenges in modelling and + data analysis. + + An important issue is to uncover meaningful temporal structure in a + network. We focus on the problem of periodicity detection in + temporal networks, by partitioning the time range of the network and + clustering the resulting subnetworks. + + For this, we leverage methods from the field of topological data + analysis and persistent homology. These methods have begun to be + employed with static graphs in order to provide a summary of + topological features, but applications to temporal networks have + never been studied in detail. + + We cluster temporal networks by computing the evolution of + topological features over time. Applying persistent homology to + temporal networks and comparing various approaches has never been + done before, and we examine their performance side-by-side with a + simple clustering algorithm. Using a generative model, we show that + persistent homology is able to detect periodicity in the topological + structure of a network. + + We define two types of topological features, with and without + aggregating the temporal networks, and multiple ways of embedding + them in a feature space suitable for machine-learning + applications. In particular, we examine the theoretical guarantees + and empirical performance of kernels defined on topological + features. + + Topological insights prove to be useful in statistical learning + applications. Combined with the recent advances in network science, + they lead to a deeper understanding of the structure of temporal + networks. \end{abstract} +\vspace*{\fill} \cleardoublepage% -\chapter*{Acknowledgements}% +\chapter{Acknowledgements}% \label{cha:acknowledgements} -Thank you! +I would like to thank my supervisors, Dr Heather Harrington, Dr Renaud +Lambiotte, and Dr Mason Porter, from the Mathematical Institute, for +their continuous support and guidance from the very beginning. They +have allowed me to pursue my interests in networks and topological +data analysis while providing me with resources, ideas, and +motivation. They remained available to answer my questions and listen +to my ideas, and provided invaluable feedback at every stage of the +project. + +I would also like to thank Dr Steve Oudot from École polytechnique, +who was the first to introduce me to the field of topological data +analysis, which led me to the original idea for the project. He was +also very helpful during the project, giving me advice and updates on +recent advances in persistent homology. + +I also want to acknowledge the students and staff of the Department of +Statistics and St Catherine's college, who always provided a +stimulating work environment, along with excellent discussions. + +Finally, my thanks go to my family and friends for their interest in +my project, because trying to explain it to people not acquainted with +the topic was, and remains, the best way to clarify my ideas and +organise my thoughts. \cleardoublepage% \tableofcontents + +\clearpage + \listoffigures -% \listoftables + +\begingroup +\let\clearpage\relax +\listofalgorithms% +\addcontentsline{toc}{chapter}{List of Algorithms} +\endgroup \clearpage @@ -98,6 +150,191 @@ Thank you! \chapter{Introduction}% \label{cha:introduction} +\section{Temporal networks analysis}% +\label{sec:temp-netw-analys} + +Networks are one of the most important mathematical concepts developed +in the last few centuries. They allow the representation of +interconnected data and complex systems. As such, the concept has been +applied to wide variety of problems, in biology and neuroscience, +physics, computer networks, and social science. In this context, +network science has emerged as a discipline of its own, combining +ideas and challenges from multiple fields of +study~\cite{newman_networks:_2010}. + +\captionsetup[figure]{labelformat=empty} + +\begin{wrapfigure}[15]{R}[.3cm]{.3\linewidth} + \centering + \vspace{-15pt} + \SetCoordinates[yAngle=45] + \begin{tikzpicture}[multilayer=3d] + \SetLayerDistance{-2} + \Plane[x=.5,y=.5,width=3,height=3,style={thin,dashed},layer=1] + \Plane[x=.5,y=.5,width=3,height=3,style={thin,dashed},layer=2] + \Plane[x=.5,y=.5,width=3,height=3,style={thin,dashed},layer=3] + \begin{Layer}[layer=1] + \node at (.5,.5)[below right] {Time $t_1$}; + \end{Layer} + \begin{Layer}[layer=2] + \node at (.5,.5)[below right] {Time $t_2$}; + \end{Layer} + \begin{Layer}[layer=3] + \node at (.5,.5)[below right] {Time $t_3$}; + \end{Layer} + + \Vertex[x=1.408,y=1.635,size=0.3,color=blue,opacity=0.7,layer=1]{0_1} + \Vertex[x=1.089,y=2.415,size=0.3,color=blue,opacity=0.7,layer=1]{1_1} + \Vertex[x=2.021,y=1.976,size=0.3,color=blue,opacity=0.7,layer=1]{2_1} + \Vertex[x=1.984,y=3.000,size=0.3,color=blue,opacity=0.7,layer=1]{3_1} + \Vertex[x=2.576,y=2.398,size=0.3,color=blue,opacity=0.7,layer=1]{4_1} + \Vertex[x=2.911,y=1.593,size=0.3,color=blue,opacity=0.7,layer=1]{5_1} + \Vertex[x=1.994,y=1.000,size=0.3,color=blue,opacity=0.7,layer=1]{6_1} + \Edge[](1_1)(3_1) + \Edge[](3_1)(4_1) + \Edge[](3_1)(4_1) + \Edge[](0_1)(5_1) + \Edge[](0_1)(5_1) + \Edge[](2_1)(5_1) + \Edge[](4_1)(5_1) + \Edge[](0_1)(6_1) + \Edge[](1_1)(6_1) + \Edge[](5_1)(6_1) + + \Vertex[x=1.408,y=1.635,size=0.3,color=blue,opacity=0.7,layer=2]{0_2} + \Vertex[x=1.089,y=2.415,size=0.3,color=blue,opacity=0.7,layer=2]{1_2} + \Vertex[x=2.021,y=1.976,size=0.3,color=blue,opacity=0.7,layer=2]{2_2} + \Vertex[x=1.984,y=3.000,size=0.3,color=blue,opacity=0.7,layer=2]{3_2} + \Vertex[x=2.576,y=2.398,size=0.3,color=blue,opacity=0.7,layer=2]{4_2} + \Vertex[x=2.911,y=1.593,size=0.3,color=blue,opacity=0.7,layer=2]{5_2} + \Vertex[x=1.994,y=1.000,size=0.3,color=blue,opacity=0.7,layer=2]{6_2} + \Edge[](1_2)(2_2) + \Edge[](0_2)(3_2) + \Edge[](2_2)(4_2) + \Edge[](2_2)(6_2) + + \Vertex[x=1.408,y=1.635,size=0.3,color=blue,opacity=0.7,layer=3]{0_3} + \Vertex[x=1.089,y=2.415,size=0.3,color=blue,opacity=0.7,layer=3]{1_3} + \Vertex[x=2.021,y=1.976,size=0.3,color=blue,opacity=0.7,layer=3]{2_3} + \Vertex[x=1.984,y=3.000,size=0.3,color=blue,opacity=0.7,layer=3]{3_3} + \Vertex[x=2.576,y=2.398,size=0.3,color=blue,opacity=0.7,layer=3]{4_3} + \Vertex[x=2.911,y=1.593,size=0.3,color=blue,opacity=0.7,layer=3]{5_3} + \Vertex[x=1.994,y=1.000,size=0.3,color=blue,opacity=0.7,layer=3]{6_3} + \Edge[](0_3)(2_3) + \Edge[](0_3)(2_3) + \Edge[](1_3)(4_3) + \Edge[](2_3)(5_3) + \Edge[](4_3)(5_3) + \Edge[](0_3)(6_3) + \Edge[](2_3)(6_3) + \Edge[](4_3)(6_3) + \end{tikzpicture} + \caption[Multilayer network.]{}% + \label{fig:multilayer} +\end{wrapfigure} + +\captionsetup[figure]{labelformat=default} + +An emerging trend in network science is the study of dynamics on +networks~\cite{holme_temporal_2012, holme_modern_2015, + porter_dynamical_2014}. Real-world systems, such as brains or social +groups, tend to evolve over time, and these changing networks have +given birth to the new field of network dynamics, where edges can +reconfigure over time. Mathematical modelling of temporal connectivity +patterns remain a difficult +problem~\cite{bassett_network_2017}. Recent advances in applied +mathematics have led to may concurrent representations, multilayer +networks~\cite{kivela_multilayer_2014} being one of the most +important. + +Temporal networks bring new challenges in size, shape, and complexity +of data analysis, but also new opportunities with the development of +new empirical methods and theoretical advances. One of these advances +is the development of generative models that can be used to infer the +dynamic mechanisms taking place in real-world +systems~\cite{bazzi_generative_2016, gauvin_randomized_2018, + petri_simplicial_2018}. + +Moreover, network theory naturally exposes many links with +topology. The purpose of networks lies in the representation of +\emph{structure}, while topology is the study of spaces and +\emph{connectedness}. As topological methods gain traction in data +science and statistical learning, they are also applied to more +complex data representations, including +networks~\cite{horak_persistent_2009, petri_topological_2013, + stolz_persistent_2017}. Topological features naturally complement +more traditional network statistics by focusing on mesoscale +structure. + + +\section{Related work}% +\label{sec:related-work} + +Topological data analysis (TDA) is a recent +field~\cite{carlsson_topology_2009}. It was originally focused on +point cloud data, with only a recent shift towards network +data~\cite{horak_persistent_2009}. Various methods have been +developed, the main one being the weight-rank clique filtration +(WRCF)~\cite{petri_topological_2013}. Other examples of application of +TDA to networks using WRCF can be found in~\cite{otter_roadmap_2017}. + +There has also been attempts to map the nodes of a network to points +in a metric space. For instance, the shortest-path distance between +nodes can be used to compute pairwise distances in the network. Note +that for this method to work properly, the network has to be +connected. Many methods can be used to build a simplicial complex from +a directed or undirected network~\cite{jonsson_simplicial_2008, + horak_persistent_2009}. + +The main starting point for this project was the introduction of TDA +for the study of temporal networks +in~\cite{price-wright_topological_2015}. In this dissertation, +topological methods are introduced to classify temporal networks +randomly generated by different models. The objective of this study +was to uncover the temporal structure of a network in order to inform +its partitioning into ``snapshots''. Different methods to partition a +network were compared for the first time, and topological features +appeared to be relevant for distinguishing various temporal +distributions. + +Finally, there has been an increasing interest in using the +topological structure of a dataset as an additional source of +information for a statistical learning model. This has led to the +development of topological descriptors suitable for use in various +learning contexts. Previous work on vectorizations and kernels on +topological features will be useful in the analysis of the structure +of temporal networks. + +\section{Contributions}% +\label{sec:contributions} + +The main contributions of this work are threefold: +\begin{itemize} +\item We make an attempt at temporal partitioning networks and + clustering the subnetworks, with immediate application for detecting + periodicity. Sliding windows and persistent homology have been used + in the context of periodicity detection + before~\cite{perea_sw1pers:_2015, perea_sliding_2017}, but never in + the context of temporal networks. +\item In general, topological methods have never been thoroughly + studied on temporal network data. The work + in~\cite{price-wright_topological_2015} is the first to introduce + the topic, but computation was limited due to the lack of available + libraries. Here, we introduce recent (from the last 2--3 years) + state-of-the-art topological methods and adapt them to temporal + networks. +\item Various methods to use topological features in a statistical + learning context and their trade-offs are exposed. The mathematical + background and practical considerations are leveraged to compare + them in the context of machine learning. +\item Finally, different topological approaches are compared. There + are different ways to build a simplicial filtration on a network, + and different manners of measuring distances between the outputs of + persistent homology in the context of machine learning. These + different methods are compared here with the objective of + periodicity detection in temporal networks. +\end{itemize} + \chapter{Graphs and Temporal Networks}% \label{cha:temporal-networks} @@ -151,10 +388,8 @@ nodes is fixed, but edges can appear or disappear at different times. In this study, we restrict ourselves to discrete time stamps. Each interaction is taken to be instantaneous. -%% TODO note about data collection, oversampling, -%% duration of interactions -\begin{defn}[Temporal network] +\begin{defn}[Temporal network]\label{defn:temp-net} A \emph{temporal network} is a tuple $G = (V, E, \mathcal{T}, \rho)$, where: \begin{itemize} @@ -192,61 +427,109 @@ convention a 0 weight corresponds to an absent edge. over time. \end{defn} -\section{Examples of applications}% -\label{sec:exampl-appl} +\section{Network statistics}% +\label{sec:network-statistics} -%% TODO +To analyse networks, network statistics are used. These are +low-dimensional summaries of important properties of a graph. Some of +them focus on local features, while some others concentrate on global +aspects. Note that the following only applies for graphs, and cannot +be used directly on temporal networks. -\section{Network partitioning}% -\label{sec:network-partitioning} +These definitions are taken from the reference work by +Newman~\cite{newman_networks:_2010}. -%% TODO clarify, organise, references +The first network statistics try to determine which vertices are the +most \emph{central}, which are the most ``important'' in the network. -Temporal networks are a very active research subject, leading to -multiple interesting problems. The additional time dimension adds a -significant layer of complexity that cannot be adequately treated by -the common methods on static graphs. +\begin{defn}[Local clustering coefficient] + The \emph{local clustering coefficient} of a vertex $v$ is defined as + \[ C(v) = \frac{\sum_{u,w\in \mathcal{V}} + a_{u,v}a_{w,v}a_{u,w}}{\sum_{u,w\in \mathcal{V}, u\neq w} + a_{u,v}a_{w,v}}. \] -Moreover, data collection can lead to large amount of noise in -datasets. Combined with large dataset sized due to the huge number of -data points for each node in the network, temporal graphs cannot be -studied effectively in their raw form. Recent advances have been made -to fit network models to rich but noisy -data~\cite{newman_network_2018}, generally using some variation on the -expectation-maximization (EM) algorithm. + The \emph{average clustering coefficient} is + \[ \overline{C} = \frac{1}{\left|\mathcal{V}\right|} + \sum_{v\in\mathcal{V}} C(v). \] +\end{defn} -One solution that has been proposed to study such temporal data has -been to \emph{partition} the time scale of the network into a sequence -of smaller, static graphs, representing all the interactions during a -short interval of time. The approach consists in subdividing the -lifetime of the network in \emph{sliding windows} of a given length. -We can then ``flatten'' the temporal network on each time interval, -keeping all the edges that appear at least once (or adding their -weights in the case of weighted networks). +\begin{defn}[Global clustering coefficient] + The \emph{global clustering coefficient} or \emph{transitivity} is + \[ C = \frac{3\times\text{number of triangles}}{\text{number of + connected triples}}. \] +\end{defn} -This partitioning is sensitive to two parameters: the length of each -time interval, and their overlap. Of those, the former is the most -important: it will define the \emph{resolution} of the study. If it is -too small, too much noise will be taken into account; if it is too -large, we will lose important information. There is a need to find a -compromise, which will depend on the application and on the task -performed on the network. In the case of a classification task to -determine periodicity, it will be useful to adapt the resolution to -the expected period: if we expect week-long periodicity, a resolution -of one day seems reasonable. +Another interesting summary is the average shortest path between +vertices. -Once the network is partitioned, we can apply any statistical learning -task on the sequence of static graphs. In this study, we will focus on -classification of time steps. This can be used to detect periodicity, -outliers, or even maximise temporal communities. +\begin{defn}[Path] + A \emph{path} between two vertices $v_0$ and $v_n$ is a sequence of + vertices $(v_0, v_1, \ldots, v_n)$ such that every consecutive pair + of vertices $(v_i, v_{i+1})$ is connected by an edge. -%% TODO Talk about partitioning methods? + The \emph{length} of a path is the number of edges traversed along + the path. The distance $l(u,v)$ between vertices $u$ and $v$ is + defined as the length of the shortest path between $u$ and $v$. +\end{defn} + +\begin{defn}[Average shortest path length] + The \emph{Average shortest path length} is defined as + \[ l = + \frac{1}{\left|\mathcal{V}\right|(\left|\mathcal{V}\right|-1)} + \sum_{u,v \in\mathcal{V}, u\neq v} l(u,v). \] +\end{defn} + +Many other centrality measures exist, the most well-known being the +eigenvector centrality, Katz centrality, and PageRank. See chapter~7 +of~\cite{newman_networks:_2010} for more details. + +% \section{Examples of applications}% +% \label{sec:exampl-appl} + +% \section{Network partitioning}% +% \label{sec:network-partitioning} + +% Temporal networks are a very active research subject, leading to +% multiple interesting problems. The additional time dimension adds a +% significant layer of complexity that cannot be adequately treated by +% the common methods on static graphs. + +% Moreover, data collection can lead to large amount of noise in +% datasets. Combined with large dataset sized due to the huge number of +% data points for each node in the network, temporal graphs cannot be +% studied effectively in their raw form. Recent advances have been made +% to fit network models to rich but noisy +% data~\cite{newman_network_2018}, generally using some variation on the +% expectation-maximization (EM) algorithm. + +% One solution that has been proposed to study such temporal data has +% been to \emph{partition} the time scale of the network into a sequence +% of smaller, static graphs, representing all the interactions during a +% short interval of time. The approach consists in subdividing the +% lifetime of the network in \emph{sliding windows} of a given length. +% We can then ``flatten'' the temporal network on each time interval, +% keeping all the edges that appear at least once (or adding their +% weights in the case of weighted networks). + +% This partitioning is sensitive to two parameters: the length of each +% time interval, and their overlap. Of those, the former is the most +% important: it will define the \emph{resolution} of the study. If it is +% too small, too much noise will be taken into account; if it is too +% large, we will lose important information. There is a need to find a +% compromise, which will depend on the application and on the task +% performed on the network. In the case of a classification task to +% determine periodicity, it will be useful to adapt the resolution to +% the expected period: if we expect week-long periodicity, a resolution +% of one day seems reasonable. + +% Once the network is partitioned, we can apply any statistical learning +% task on the sequence of static graphs. In this study, we will focus on +% classification of time steps. This can be used to detect periodicity, +% outliers, or even maximise temporal communities. \chapter{Topological Data Analysis and Persistent Homology}% \label{cha:tda-ph} -%% TODO references - \section{Basic constructions}% \label{sec:basic-constructions} @@ -281,7 +564,7 @@ higher-dimensional generalization of a graph. The building blocks of this representation is the \emph{simplex}, which is the convex hull of an arbitrary set of points. Examples of simplices include single points, segments, triangles, and tetrahedrons -(in dimensions 0, 1,, 2, and 3 respectively). +(in dimensions 0, 1, 2, and 3 respectively). \begin{defn}[Simplex] A \emph{$k$-dimensional simplex} $\sigma = [x_0,\ldots,x_k]$ is the @@ -335,7 +618,7 @@ simplices include single points, segments, triangles, and tetrahedrons \caption{Triangle} \end{subfigure}% % - \caption{Examples of simplices}% + \caption{Examples of simplices.}% \label{fig:simplex} \end{figure} @@ -391,8 +674,9 @@ structure of the metric space. \draw (5) -- (6) -- (4) -- (7); \end{scope} \end{tikzpicture} - \caption{Example of a simplicial complex that has two connected - components, two 3-simplices, and one 5-simplex.}% + \caption[Example of a simplicial complex.]{Example of a simplicial + complex that has two connected components, two 3-simplices, and + one 5-simplex.}% \label{fig:simplical-complex} \end{figure} @@ -400,15 +684,150 @@ The notion of simplicial complex is closely related to that of a hypergraph. One important distinction lies in the fact that a subset of a hyperedge is not necessarily a hyperedge itself. +\subsection{Simplicial homology}% +\label{sec:simplicial-homology} + Using these definitions, we can define homology on simplicial -complexes. %% TODO add reference for more details/do it myself? +complexes~\cite{edelsbrunner_computational_2010, + chazal_introduction_2017}. In this section, we restrict to homology +with coefficients in $\mathbb{Z}_2$, the field with two elements. + +\begin{defn}[$k$-chains] + Let $K$ be a finite simplicial complex, and $p$ a non-negative + integer. The space of \emph{$k$-chains} $C_p(K)$ of $K$ is the set + of formal sums of $p$-simplices of $K$. More precisely, it is the + $\mathbb{Z}_2$-vector space spanned by the $p$-simplices of $K$. +\end{defn} + +Since the coefficients of $C_p(K)$ are in $\mathbb{Z}_2$, a $p$-chain +is simply a finite collection of $p$-simplices. The sum of two +$k$-chains is the symmetric difference of the two chains, i.e.\ the +collection of $p$-simplices that belong to either, but not both, of +the chains. + +\begin{defn}[Boundary] + The \emph{boundary} of a $p$-simplex $\sigma$ is the $(p-1)$-chain + \[ \partial_p(\sigma) := \sum_{\tau\in K_{p-1},\; \tau\subset\sigma} + \tau, \] where $K_{p-1}$ is the set of $(p-1)$-simplices of $K$. +\end{defn} + +As the $p$-simplices form a basis of $C_p(K)$, $\partial_p$ can be +extended into a linear map from $C_p(K)$ to $C_{p-1}(K)$, called the +\emph{boundary operator}. The elements of the kernel +$\mathrm{Ker}(\partial_p)$ are called the \emph{$p$-cycles} of +$K$. The image $\mathrm{Im}(\partial_p)$ is the space of +\emph{$p$-boundaries} of $K$. + +\begin{lem}\label{lem:boundary} + The image of $\partial_{p+1}$ is a subset of the kernel of + $\partial_p$. +\end{lem} + +\begin{proof} + The boundary of a boundary is always empty. To see this, consider + the boundary of a $(p+1)$-simplex $\sigma$. The boundary of $\sigma$ + consists of all $p$-faces of $\sigma$. The boundary of this boundary + will contain each $(p-1)$-face of $\sigma$ twice, and since $1+1=0$ + in $\mathbb{Z}_2$, we have that + \[ \partial_{p} \circ \partial_{p+1} \equiv 0. \] + + This implies directly that + $\mathrm{Im}(\partial_{p+1}) \subset \mathrm{Ker}(\partial_p)$. +\end{proof} + +\begin{defn}[Homology] + For any $p\in\mathbb{N}$, the \emph{$p$-th (simplicial) homology + group} of a simplicial complex $K$ is the quotient vector space + \[ H_p(K) := \mathrm{Ker}(\partial_{p}) / + \mathrm{Im}(\partial_{p+1}). \] + + The dimension $\beta_p(K)$ of $H_p(K)$ is called the \emph{$p$-th + Betti number} of $K$. +\end{defn} + +Let us close this overview of simplicial homology by a look at induced +maps. Let $K$ and $L$ be two simplicial complexes and $f: K \mapsto L$ +a simplicial map between them. Since $f$ maps linearly each simplex of +$K$ to a simplex of $L$, we can extend it to map a chain of $K$ to a +chain of $L$ of the same dimension. If $c = \sum a_i \sigma_i$ is a +$p$-chain in $K$, we can define $f_\#(c) = \sum a_i \tau_i$, where +$\tau_i = f(\sigma_i)$ if it has dimension $p$ and $\tau_i = 0$ if +$f(\sigma_i)$ has dimension less than $p$. + +\begin{figure}[!ht] + \centering + \begin{tikzpicture} + \node (l dots left) at (0,0) {\ldots}; + \node (cpl) at (3,0) {$C_p(L)$}; + \node (cp1l) at (6,0) {$C_{p-1}(L)$}; + \node (l dots right) at (9,0) {\ldots}; + \node (k dots left) at (0,2) {\ldots}; + \node (cpk) at (3,2) {$C_p(K)$}; + \node (cp1k) at (6,2) {$C_{p-1}(K)$}; + \node (k dots right) at (9,2) {\ldots}; + \draw[->] (l dots left) -- (cpl) node [above,midway] {$\partial_{p+1}$}; + \draw[->] (cpl) -- (cp1l) node [above,midway] {$\partial_{p}$}; + \draw[->] (cp1l) -- (l dots right) node [above,midway] {$\partial_{p-1}$}; + \draw[->] (k dots left) -- (cpk) node [above,midway] {$\partial_{p+1}$}; + \draw[->] (cpk) -- (cp1k) node [above,midway] {$\partial_{p}$}; + \draw[->] (cp1k) -- (k dots right) node [above,midway] {$\partial_{p-1}$}; + \draw[->] (cpk) -- (cpl) node [right,midway] {$f_\#^{p}$}; + \draw[->] (cp1k) -- (cp1l) node [right,midway] {$f_\#^{p-1}$}; + \end{tikzpicture} + \caption{Induced maps and boundary operators.}% + \label{fig:induced-map} +\end{figure} + +\begin{prop} + $f_\#$ commutes with the boundary operator: + \[ f_\# \circ \partial_K = \partial_L \circ f_\#. \] +\end{prop} + +\begin{proof} + Consider $f_\#^p : C_p(K) \mapsto C_p(L)$, and let + $c = \sum a_i \sigma_i \in C_p(K)$. If $f(\sigma_i)$ has dimension + $p$, then all $(p-1)$-faces of $\sigma_i$ map to the corresponding + $(p-1)$-faces of $\tau_i$, and the proposition follows. On the other + hand, if $f(\sigma_i)$ has dimension less than $p$, then the + $(p−1)$-faces of σf $\sigma_i$ map to simplices of dimension less + than $p−1$, with the possible exception of exactly two $(p−1)$-faces + whose images coincide and cancel each other. So we have that + $\partial_L(f_\#(\sigma_i)) = f_\#(\partial_K(\sigma_i)) = + 0$. (See~\cite{edelsbrunner_computational_2010} for details.) +\end{proof} + +\begin{cor} + $f_\#$ maps cycles to cycles, and boundaries to boundaries. +\end{cor} + +Therefore, $f_\#$ defines a map over quotients, called the +\emph{induced map on homology} \[ f_*^p : H_p(K) \mapsto H_p(L). \] + +\begin{prop}\label{prop:functor} + $f \mapsto f_*^p$ is a functor: + \begin{itemize} + \item if $f = \mathrm{id}_X$, then $f_*^p = \mathrm{id}_{H_p(X)}$, + \item if + $X \overset{f}{\longrightarrow} Y \overset{g}{\longrightarrow} Z$, + then ${(g \circ f)}_* = g_* \circ f_*$. + \end{itemize} +\end{prop} + +%% homotopy equivalence? + +The derivation of simplicial homology in this section used the field +$\mathbb{Z}_2$. It is however possible to define homology over any +field. The definition of the boundary operator needs to be adapted to +ensure that the \autoref{lem:boundary} remains true, even when +$1 \neq -1$. In this dissertation, we consider only persistent +homology on $\mathbb{Z}_2$, as it is the field used in our +implementation. It is important to note however that changing the +field of the vector spaces can affect the homology and therefore the +topological features detected~\cite{zomorodian_computing_2005}. \subsection{Filtrations}% \label{sec:filtrations} -%% TODO rewrite it using the Cech complex as an introductory example, -%% to understand the problem with scale - If we consider that a simplicial complex is a kind of ``discretization'' of a subset of a metric space, we realise that there must be an issue of \emph{scale}. For our analysis to be @@ -417,8 +836,100 @@ the optimal scale parameter to capture the adequate topological structure, without taking into account some small perturbations, nor ignoring some important smaller features. -One possible solution to these problems is to consider all scales at -once. This is the objective of \emph{filtered simplicial complexes}. +To illustrate this, let us take the example of the Čech complex, one +of the most important tools to build a simplicial complex from a +metric space. + +\begin{defn}[Nerve] + Let $\mathcal{S} = {(S_i)}_{i\in I}$ be a non-empty collection of + sets. The \emph{nerve} of $\mathcal{S}$ is the simplicial complex + whose vertices are the elements of $I$ and where + $(i_0, \ldots, i_k)$ is a $k$-simplex if, and only if, + $S_0 \cap \cdots \cap S_k \neq \emptyset$. +\end{defn} + +\begin{defn}[Čech complex] + Let $X$ be a point cloud in an arbitrary metric space, and + $\varepsilon > 0$. The \emph{Čech complex} + $\check{C}_\varepsilon(X)$ is the nerve of the set of + $\varepsilon$-balls centred on the points in $X$. +\end{defn} + +% By the Nerve theorem~\cite{edelsbrunner_computational_2010}, we know +% that for any point cloud $X$ and any $\varepsilon > 0$, +% $\check{C}_\varepsilon(X)$ and $X$ have the same homology. + +An example construction of a Čech complex is represented +on~\autoref{fig:cech-complex}. The simplicial complex depends on the +value of $\varepsilon$. To adequately represent the topological +structure of the underlying point cloud, it is necessary to consider +all possible values of $\varepsilon$ in order to capture all the +topological features. + +\begin{figure}[ht] + \centering + \begin{tikzpicture}[scale=1.1,every node/.style={transform shape}] + \tikzstyle{point}=[circle,thick,draw=black,fill=blue!30,% + inner sep=0pt,minimum size=5pt] + \node (a)[point] {}; + \node (b)[point,right=1.5cm of a] {}; + \node (c)[point,above right=1cm and .7cm of a] {}; + \node (d)[point,above=1.5cm of c] {}; + \node (e)[point,above right=.8cm and 1.55cm of c] {}; + \node (f)[point,above right=.5cm and 2.5cm of b] {}; + + \def\circlea{(a) circle (1cm)} + \def\circleb{(b) circle (1cm)} + \def\circlec{(c) circle (1cm)} + \def\circled{(d) circle (1cm)} + \def\circlee{(e) circle (1cm)} + \def\circlef{(f) circle (1cm)} + + \draw[color=gray,dashed,thick] \circlea; + \draw[color=gray,dashed,thick] \circleb; + \draw[color=gray,dashed,thick] \circlec; + \draw[color=gray,dashed,thick] \circled; + \draw[color=gray,dashed,thick] \circlee; + \draw[color=gray,dashed,thick] \circlef; + + \begin{scope} + \clip\circlea; + \fill[pattern=north east lines,pattern color=blue!40]\circleb; + \fill[pattern=north east lines,pattern color=blue!40]\circlec; + \end{scope} + \begin{scope} + \clip\circlec; + \fill[pattern=north east lines,pattern color=blue!40]\circleb; + \fill[pattern=north east lines,pattern color=blue!40]\circled; + \fill[pattern=north east lines,pattern color=blue!40]\circlee; + \end{scope} + \begin{scope} + \clip\circled; + \fill[pattern=north east lines,pattern color=blue!40]\circlee; + \end{scope} + + \draw[<->] (b) -- +(1cm,0cm) node[left=.45cm,anchor=south] {$\varepsilon$}; + + \node (a1)[point,right=7cm of a] {}; + \node (b1)[point,right=1.5cm of a1] {}; + \node (c1)[point,above right=1cm and .7cm of a1] {}; + \node (d1)[point,above=1.5cm of c1] {}; + \node (e1)[point,above right=.8cm and 1.55cm of c1] {}; + \node (f1)[point,above right=.5cm and 2.5cm of b1] {}; + \begin{scope}[on background layer] + \draw[thick,fill=blue!15] (a1.center) -- (b1.center) -- (c1.center) -- cycle; + \draw[thick] (c1.center) -- (d1.center) -- (e1.center) -- cycle; + \end{scope} + \end{tikzpicture} + \caption[Example of a point cloud and the corresponding Čech + complex.]{Example of a point cloud (left), and the corresponding + Čech complex at level $\varepsilon$ (right). Dashed circles + represent the $\varepsilon$-balls used to construct the simplicial + complex.}% + \label{fig:cech-complex} +\end{figure} + +This is the objective of \emph{filtered simplicial complexes}. \begin{defn}[Filtration]\label{defn:filt} A \emph{filtered simplicial complex}, or simply a \emph{filtration}, @@ -430,46 +941,64 @@ once. This is the objective of \emph{filtered simplicial complexes}. \end{itemize} \end{defn} +To continue the example of Čech filtrations, one can build a sequence +of simplicial complexes for each value of $\varepsilon > 0$. Due to +their construction, Čech complexes on a point cloud $X$ respect the +essential inclusion property: +\[ \forall \varepsilon, \varepsilon' > 0,\quad \varepsilon < \varepsilon' + \implies \check{C}_\varepsilon(X) \subseteq + \check{C}_{\varepsilon'}(X). \] + \section{Persistent Homology}% \label{sec:persistent-homology} We can now compute the homology for each step in a filtration. This leads to the notion of \emph{persistent - homology}~\cite{carlsson_topology_2009,zomorodian_computing_2005}, + homology}~\cite{carlsson_topology_2009, zomorodian_computing_2005}, which gives all the information necessary to establish the topological structure of a metric space at multiple scales. \begin{defn}[Persistent homology] The \emph{$p$-th persistent homology} of a simplicial complex $K = {(K_i)}_{i\in I}$ is the pair - $(\{H_p(K_i)\}_{i\in I}, \{f_{i,j}\}_{i,j\in I, i\leq j})$, where + $({\{H_p(K_i)\}}_{i\in I}, {\{f_{i,j}\}}_{i,j\in I, i\leq j})$, where for all $i\leq j$, $f_{i,j} : H_p(K_i) \mapsto H_p(K_j)$ is induced by the inclusion map $K_i \mapsto K_j$. \end{defn} -The functions $f_{i,j}$ allow one to link generators in each -successive homology space in a filtration. Because each generator -corresponds to a topological feature (connected component, hole, void, -and so on, depending on the dimension $p$), we can determine whether -it survives in the next step of the filtration. We can also determine -when each feature is born and when it dies (if it dies at all). The -couples of intervals (birth time, death time) depends on the choice of -basis for each homology space $H_p(K_i)$. However, by the Fundamental -Theorem of Persistent Homology~\cite{zomorodian_computing_2005}, we -can choose basis vectors in each homology space such that the -collection of half-open intervals is well-defined and unique. This -construction is called a \emph{barcode}~\cite{carlsson_topology_2009}. +By functoriality (\autoref{prop:functor}), +$f_{k,j} \circ f_{i,k} = f_{i,j}$. Therefore, the functions $f_{i,j}$ +allow us to link generators in each successive homology space in a +filtration. + +Because each generator corresponds to a topological feature (connected +component, hole, void, and so on, depending on the dimension $p$), we +can determine whether it survives in the next step of the +filtration. We say that $x \in H_p(K_i)$ is \emph{born} in $H_p(K_i)$ +if it is not in the image of $f_{i-1,i}$. We say that $x$ \emph{dies} +in $H_p(K_j)$ if $j > i$ is the smallest index such that +$f_{i,j}(x) = 0$. The half-open interval $[i, j)$ represents the +lifetime of $x$. If $f_{i,j}(x) \neq 0$ for all $j > i$, we say that +$x$ lives forever and its lifetime is the interval $[i, \infty)$. + +The couples of intervals (birth time, death time) depends on the +choice of basis for each homology space $H_p(K_i)$. However, by the +Fundamental Theorem of Persistent +Homology~\cite{zomorodian_computing_2005}, we can choose basis vectors +in each homology space such that the collection of half-open intervals +is well-defined and unique. This construction is called a +\emph{barcode}~\cite{carlsson_topology_2009}. \section{Topological summaries: barcodes and persistence diagrams}% \label{sec:topol-summ} -%% TODO need more context - -To interpret the results of the persistent-homology computation, we -need to compare the output for a particular data set to a suitable -null model. For this, we need some kind of similarity measure between -barcodes and a way to evaluate the statistical significance of the -results. +Although it contains relevant topological information, the persistent +homology defined in the previous section cannot be used directly in +statistical methods. \emph{Topological summaries} are a compact +representation of persistent homology as elements of a metric +space. This is particularly useful in the context of statistical +analysis, e.g.\ when one needs to compare the output of a given +dataset to a null model. One possible approach is to define a space in which we can project barcodes and study their geometric properties. One such space is the @@ -501,7 +1030,7 @@ be caused by small perturbations in the data. One can build a persistence diagram from a barcode by taking the union of the multiset of (birth, death) couples with the diagonal -$\Delta$. \autoref{fig:pipeline} summarises the entire pipeline. +$\Delta$. \autoref{fig:ph-pipeline} summarises the entire pipeline. \begin{figure}[ht] \centering @@ -519,8 +1048,8 @@ $\Delta$. \autoref{fig:pipeline} summarises the entire pipeline. \draw[->] (dgm.east) -- (interp.west); \end{tikzpicture} - \caption{Persistent homology pipeline}% - \label{fig:pipeline} + \caption{Persistent homology pipeline.}% + \label{fig:ph-pipeline} \end{figure} One can define an operator $\dgm$ as the first two steps in the @@ -530,7 +1059,7 @@ metric space, via persistent homology on a filtered complex. We can now define several distances on the space of persistence diagrams. -\begin{defn}[Wasserstein distance] +\begin{defn}[Wasserstein distance]\label{defn:wasserstein-dist} The \emph{$p$-th Wasserstein distance} between two diagrams $X$ and $Y$ is \[ W_p[d](X, Y) = \inf_{\phi:X\mapsto Y} \left[\sum_{x\in X} {d\left(x, \phi(x)\right)}^p\right] \] @@ -541,7 +1070,7 @@ diagrams. $Y$. \end{defn} -\begin{defn}[Bottleneck distance] +\begin{defn}[Bottleneck distance]\label{defn:bottleneck} The \emph{bottleneck distance} is defined as the infinite Wasserstein distance where $d$ is the uniform norm: $d_B = W_\infty[L_\infty]$. @@ -552,13 +1081,43 @@ triangle inequality. However, it is not a true distance, as one can come up with two distinct diagrams with bottleneck distance 0, even on multisets that do not touch the diagonal $\Delta$. +\begin{figure}[ht] + \centering + \begin{tikzpicture}[dot/.style={draw,circle,inner sep=0pt,minimum size=4pt}] + \draw[thick,->] (-0.3,0) -- (6,0) node [right] {birth}; + \draw[thick,->] (0,-0.3) -- (0,6) node [above right] {death}; + \draw[very thick,dashed] (0,0) -- (5.5,5.5); + \node (b1) [dot,fill=blue!70] at (1,2) {}; + \node (r1) [dot,fill=red!70] at (1,2.5) {}; + \draw (b1) -- (r1); + \node (b2) [dot,fill=blue!70] at (1.5,4.5) {}; + \node (r2) [dot,fill=red!70] at (2,5) {}; + \draw (b2) -- (r2); + \node (b3) [dot,fill=blue!70] at (3,5.2) {}; + \node (r3) [dot,fill=red!70] at (2.5,5.5) {}; + \draw (b3) -- (r3); + \node (b4) [dot,fill=blue!70] at (2,3) {}; + \draw (b4) -- (2.5,2.5); + \node (b5) [dot,fill=blue!70] at (4.7,5.3) {}; + \draw (b5) -- (5,5); + \node (b6) [dot,fill=blue!70] at (0.8,1.2) {}; + \draw (b6) -- (1,1); + \node (r4) [dot,fill=red!70] at (2.8,3.2) {}; + \draw (r4) -- (3,3); + \node (r5) [dot,fill=red!70] at (3.4,4.2) {}; + \draw (r5) -- (3.8,3.8); + \end{tikzpicture} + \caption{Bottleneck distance between two diagrams.}% + \label{fig:bottleneck} +\end{figure} + \section{Stability}% \label{sec:stability} One of the most important aspects of topological data analysis is that it is \emph{stable} with respect to small perturbations in the data. More precisely, the second step of the pipeline -in~\autoref{fig:pipeline} is Lipschitz with respect to a suitable +in~\autoref{fig:ph-pipeline} is Lipschitz with respect to a suitable metric on filtered complexes and the bottleneck distance on persistence diagrams~\cite{cohen-steiner_stability_2007,chazal_persistence_2014}. First, @@ -583,15 +1142,46 @@ property~\cite{cohen-steiner_stability_2007,chazal_persistence_2014}. \section{Algorithms and implementations}% \label{sec:algor-impl} -%% TODO -\cite{morozov_dionysus:_2018,bauer_ripser:_2018,reininghaus_dipha_2018,maria_gudhi_2014} +Many algorithms have been developed to compute persistent +homology. The first one developed, and by far the most commonly used +is the so-called standard algorithm, introduced for the field +$\mathbb{Z}_2$ in~\cite{edelsbrunner_topological_2000}, and for +general fields in~\cite{zomorodian_computing_2005}. This algorithm +operates on the sequentially on the column of a boundary matrix. Its +complexity is therefore cubic in the number of simplices in the worst +case. It has been proven that this bound is +hard~\cite{morozov_persistence_2005}. -\section{Discussion}% -\label{sec:discussion} +Many algorithms have since been developed to deliver heuristic +speed-ups in the case of sparse matrices. There are both sequential +algorithms, such as the dual algorithm~\cite{de_silva_persistent_2011, + de_silva_dualities_2011}, and algorithms that introduce parallelism +in the computation, such as the distributed +algorithm~\cite{mcgeoch_distributed_2014}. -%% TODO +These algorithms have been implemented in many publicly-available +implementations in the last few years. For a complete review and +benchmarks of these implementations, +see~\cite{otter_roadmap_2017}. Here, we focus on implementations that +provide a Python interface and implement common data structures, such +as filtrations and persistence diagrams. State-of-the-art libraries +include Ripser~\cite{bauer_ripser:_2018}, +DIPHA~\cite{reininghaus_dipha_2018}, GUDHI~\cite{maria_gudhi_2014}, +and Dionysus~\cite{morozov_dionysus:_2018}. GUDHI and Dionysus are +under active development, with new versions released recently, +exposing a complete Python API and implementing various algorithms, +including multifield persistence and cohomology. -%% information thrown away in filtrations and in PH +In this project, Dionysus~2 has been selected for its ease of use, +good documentation, and good performance~\cite{otter_roadmap_2017}. In +this project, we only use persistent homology over the field +$\mathbb{Z}_2$. Dionysus is also one of the few libraries to implement +zigzag persistence (\autoref{sec:zigzag-persistence}). + +% \section{Discussion}% +% \label{sec:discussion} + +% information thrown away in filtrations and in PH \chapter{Topological Data Analysis on Networks}% @@ -604,12 +1194,35 @@ We now consider the problem of applying persistent homology to network data. An undirected network is already a simplicial complex of dimension 1. However, this is not sufficient to capture enough topological information; we need to introduce higher-dimensional -simplices. One method is to project the nodes of a network onto a -metric space~\cite{otter_roadmap_2017}, thereby transforming the -network data into a point-cloud data. For this, we need to compute the -distance between each pair of nodes in the network (e.g.\ with the -shortest-path distance). This also requires the network to be -connected. %% TODO defn of connected? +simplices. If the network is connected, one method is to project the +nodes of a network onto a metric space~\cite{otter_roadmap_2017}, +thereby transforming the network data into a point-cloud data. For +this, we need to compute the distance between each pair of nodes in +the network (e.g.\ with the shortest-path distance). + +Various methods to project nodes onto a metric space (called +\emph{graph embeddings}) are +available~\cite{fouss_algorithms_2016}. These mapping try to preserve +the structure of the network as much as possible, e.g.\ by ensuring +that neighbours in the network are neighbours in the metric space +(according to the distance in that space), and vice-versa. A few +methods worth mentioning in this area are \emph{spectral methods}, +which define the mapping according to the eigenvectors of a matrix +constructed from the graph. These methods have the advantage of +minimizing a well-defined criterion, which often admits an exact +solution, and can often be computed +exactly~\cite{fouss_algorithms_2016}. They include kernel principal +components analysis, multidimensional scaling, Markov diffusion maps +and Laplacian eigenmap. Other methods are \emph{latent space methods}, +which produce an embedding using a physical analogy, such as spring +networks and attractive forces. These methods are often used for graph +drawing (i.e.\ embedding in 2 or 3-dimensional spaces), but can only +be approximated for large networks~\cite{fouss_algorithms_2016}. + +Using these graph embeddings, one can get a point cloud in a Euclidean +space, and build a simplicial complex using one of the various methods +developed for point clouds. One such example is the Čech complex +(\autoref{sec:filtrations}). Another common method, for weighted networks, is called the \emph{weight rank-clique filtration} @@ -632,12 +1245,9 @@ itself a filtered simplicial complex (\autoref{defn:filt}), because a subset of a clique is necessarily a clique itself, and the same is true for the intersection of two cliques. -This leads to a first possibility for applying persistent homology to -temporal networks. It is possible to segment the lifetime of a network -into sliding windows, creating a time-independent graph on each window -by retaining only the edges available during the time interval. We can -then apply WRCF on each graph in the sequence, obtaining a filtered -complex for each window, to which we can then apply persistent +This leads to one of the possibilities for applying persistent +homology to temporal networks. One can apply WRCF on a network, +obtaining a filtered complex, to which we can then apply persistent homology. This method can quickly become very computationally expensive, as @@ -645,53 +1255,68 @@ finding all maximal cliques (e.g.\ using the Bron--Kerbosch algorithm) is a complicated problem, with an optimal computational complexity of $\mathcal{O}\big(3^{n/3}\big)$~\cite{tomita_worst-case_2006}. In practice, one often restrict the search to cliques of dimension less -than or equl to a certain bound $d_M$. With this restriction, the new +than or equal to a certain bound $d_M$. With this restriction, the new simplicial complex is homologically equivalent to the original: they have the same homology groups up to $H_{d_M-1}$. -%% TODO rewrite this paragraph -This method is sensitive to the choice of sliding windows on the time -scale. The width and the overlap of the windows can completely change -the networks created and their topological features. Too small a -window, and the network becomes too small to have any significant -topological properties, too large, and we lose important information -in the evolution of the network over time. - \section{Zigzag persistence}% \label{sec:zigzag-persistence} -The standard algorithm to compute persistent homology -(see~\autoref{sec:persistent-homology}) relies on the fact that -filtrations (see~\autoref{defn:filt}) are nested sequences of -simplicial complexes: -\[ \cdots \subseteq K_{i-1} \subseteq K_i \subseteq K_{i+1} \subseteq - \cdots \] +The persistent homology methods exposed in the previous sections +operate on filtrations which are nested sequences of simplicial +complexes: +\[ \cdots \longrightarrow K_{i-1} \longrightarrow K_i \longrightarrow + K_{i+1} \longrightarrow \cdots, \] where each $\longrightarrow$ +represents an inclusion map. -One can now create an independent filtration (e.g.\ with WRCF) for -each time step. The issue is that the topological features will be -orthogonal to the time dimension. +As we have seen in the previous section, filtrations can be built on +networks. Computing persistent homology therefore relies on +aggregating temporal networks, and then building a sequence of nested +simplicial complexes orthogonal to the time dimension. -Another possibility is to create a filtration along the time -dimension. The issue in this case is that the sequence is no longer -nested (except for additive or dismantling temporal networks, -see~\autoref{defn:additive}). +Another approach would be to use the existing temporal sequence in the +network to build the filtration. The issue in this case is that the +sequence is no longer nested, as edges can be added or removed at each +time step (except for additive or dismantling temporal networks, +see~\autoref{defn:additive}). The development of \emph{zigzag + persistence}~\cite{carlsson_zigzag_2008, carlsson_zigzag_2009} +solves this issue by introducing a novel way to compute persistent +homology on sequences of complexes that are no longer nested: +\[ \cdots \longleftrightarrow K_{i-1} \longleftrightarrow K_i + \longleftrightarrow K_{i+1} \longleftrightarrow \cdots, \] where +each $\longleftrightarrow$ represents an inclusion map oriented +forwards or backwards. -The solution to consider the time dimension is provided by -\emph{zigzag persistence}~\cite{carlsson_zigzag_2009}, which allows -one to compute persistence on alternating nested sequences: -\[ \cdots \supseteq K_{i-1} \subseteq K_i \supseteq K_{i+1} \subseteq - \cdots \] +To build this sequence from a temporal network, one can build a clique +complex at each time step. Edge additions and deletions will translate +to simplex additions and deletions in the sequence of simplicial +complexes. More details of this implementation is provided +in~\autoref{sec:zigzag-persistence-1}. -This sequence can in turn be computed from a temporal network by -computing the union of each pair of consecutive time steps, -constructing an alternating sequence. +Note that zigzag persistence is related to the more general concept of +\emph{multi-parameter persistence}~\cite{carlsson_theory_2009, + dey_computing_2014}, where simplicial complexes can be filtered with +more than one parameter. It is an active area of research, especially +as the fundamental theorem of persistent homology is no longer valid +with more than one parameter, and there are significant challenges in +the visualization of ``barcodes'' for 2-parameter persistent +homology~\cite{otter_roadmap_2017}. -Zigzag persistence is a special case of the more general concept of -\emph{multi-parameter - persistence}~\cite{carlsson_theory_2009,dey_computing_2014}, where -filtrations can encompass multiple parameters. +The complexity of the zigzag algorithm is cubic in the maximum number +of simplices in the complex~\cite{carlsson_zigzag_2009}, which is +equivalent to the worst-case complexity of the standard algorithm for +persistent homology (\autoref{sec:algor-impl}). In practice however, +zigzag computation tend to be much longer than their standard +counterparts. Computing zigzag persistence on a temporal network is +more costly than computing persistent homology on the weight rank +clique filtration of the aggregated graph. -%% Note about libraries implementing zigzag persistence: Dionysus +The library Dionysus~\cite{morozov_dionysus:_2018} is the only one to +implement zigzag persistence at the time of this writing. As +implementation of the zigzag algorithm is not +straightforward~\cite{carlsson_zigzag_2009, maria_computing_2016}, +Dionysus was the most logical option for the topological study of +temporal networks. \chapter{Persistent Homology for Machine-Learning Applications}% \label{cha:pers-homol-mach} @@ -713,18 +1338,31 @@ possible to define some statistical summaries (means, medians, confidence intervals) on these spaces~\cite{turner_frechet_2014,munch_probabilistic_2015}. -%% TODO REFERENCES - The issue with this approach is that metric spaces do not offer enough algebraic structure to be amenable to most machine-learning -techniques. One of the most recent development in the study of -topological summaries has been to find mappings between the space of -persistence diagrams and Banach spaces. +techniques. Many of these methods, such as principal-components +analysis (PCA) and support vector machines (SVMs) require a Hilbert +structure on the feature space~\cite{carriere_sliced_2017, + chazal_persistence_2014}. Equipped with this structure, one can then +define common operations such as addition, average or scalar product +on features, which then facilitate their use in machine learning. One +of the most recent development in the study of topological summaries +has been to find mappings between the space of persistence diagrams +and Banach spaces\cite{adams_persistence_2017, + bubenik_statistical_2015, kwitt_statistical_2015, + kusano_kernel_2017}. (The definitions of common topological +structures can be found in \autoref{cha:topology}.) \section{Vectorization methods}% \label{sec:vect-meth} -%% TODO +The first possibility is to build an explicit feature map. Each +persistence diagram is projected into a vector of $\mathbb{R}^n$, on +which one can then build a suitable Hilbert structure. + +The main examples in this category are persistence +landscapes~\parencite{bubenik_statistical_2015} and persistence +images~\cite{adams_persistence_2017}. \subsection{Persistence landscapes} @@ -740,13 +1378,13 @@ diagram. Moreover, a mean of persistence landscapes does not necessarily have a corresponding persistence diagram. \begin{defn}[Persistence landscape] - The persistence landscape of a diagram $D = \{(b_i,d_i)\}_{i=1}^n$ + The persistence landscape of a diagram $D = {\{(b_i,d_i)\}}_{i=1}^n$ is the set of functions $\lambda_k: \mathbb{R} \mapsto \mathbb{R}$, for $k\in\mathbb{N}$, such that - \[ \lambda_k(x) = k\text{-th largest value of } \{f_{(b_i, - d_i)}(x)\}_{i=1}^n, \] (and $\lambda_k(x) = 0$ if the $k$-th - largest value does not exist), where $f_{(b,d)}$ is a - piecewise-linear function defined by: + \[ \lambda_k(x) = k\text{-th largest value of } {\{f_{(b_i, + d_i)}(x)\}}_{i=1}^n, \] (and $\lambda_k(x) = 0$ if the $k$-th +largest value does not exist), where $f_{(b,d)}$ is a piecewise-linear +function defined by: \[ f_{(b,d)} = \begin{cases} 0,& \text{if }x \notin (b,d),\\ @@ -765,36 +1403,1552 @@ statistical computations. \subsection{Persistence images} -\cite{adams_persistence_2017} +Persistence images~\cite{adams_persistence_2017} consist in a +convolution of the persistence diagram with a probability +distribution, followed by a discretization of the resulting +distribution in order to obtain a finite-dimensional vector. Most of +the following section is derived from the original +paper~\cite{adams_persistence_2017}. -\subsection{Tropical and arctic semirings} +\begin{defn}[Persistence surface] + Let $B$ be a persistence diagram, and + $T : \mathbb{R}^2\mapsto\mathbb{R}^2$ the linear transformation + $T(x,y) = (x, y-x)$. Let $\phi_u:\mathbb{R}^2\mapsto\mathbb{R}$ be a + differentiable probability density function with mean + $u\in\mathbb{R}^2$, and $f$ a non-negative weighting function which + is zero along the horizontal axis, continuous, and piecewise + differentiable. -\cite{kalisnik_tropical_2018} + The \emph{persistence surface} associated to $B$ is the function + $\rho_B:\mathbb{R}^2\mapsto\mathbb{R}$ such that + \[ \rho_B(z) = \sum_{u\in T(B)} f(u) \phi_u(z). \] +\end{defn} + +Then, one needs to reduce the persistence surface to a +finite-dimensional vector by discretizing a subdomain of $\rho_B$ and +integrating it over each region. + +\begin{defn}[Persistence image] + Let $\rho_B$ be the persistence surface of a persistence diagram + $B$. We fix a grid on the plane with $n$ cells (called + \emph{pixels}). The \emph{persistence image} of $B$ is the + collection of pixels, where for each cell $p$, + \[ {I(\rho_B)}_p = \iint_p \rho_B \diff y \diff x. \] +\end{defn} + +There are three parameters: +\begin{itemize} +\item the resolution of the grid overlaid on the persistence surface, +\item the probability distribution, which is often taken as a Gaussian + distribution centred on each point (one still needs to choose an + appropriate variance), +\item the weight function, which must be zero on the horizontal axis + (which corresponds to the diagonal $\Delta$ before transformation by + the function $T$), continuous, and piecewise differentiable in order + for the stability results to hold. Generally, weighting functions + are taken non-decreasing in $y$ in order to weight points of higher + persistence more heavily. +\end{itemize} + +All of these choices are non-canonical, but the classification +accuracy on most tasks seem to be robust to the choice of resolution +and variance of the Gaussian +distribution~\cite{zeppelzauer_topological_2016}. + +It is also important to note that points with infinite persistence are +ignored by the weighting function $f$. Persistence images are +therefore not suitable in applications where these features can be +important to consider. + +Persistence images are stable with respect to the 1-Wasserstein +distance between persistence diagrams (and with respect to the $L^1$, +$L^2$, and $L^\infty$ distances between +images)~\cite{adams_persistence_2017}. + +In practice, persistence images are interesting because they project +persistence diagrams in a Euclidean space. Compared to persistence +landscape, one can apply a broader range of machine-learning +techniques. It has also been observed that persistence images +outperform performance landscapes in many classification tasks, with a +comparable computational efficiency~\cite{adams_persistence_2017}. + +% \subsection{Tropical and arctic semirings} + +% \cite{kalisnik_tropical_2018} \section{Kernel-based methods}% \label{sec:kernel-based-methods} -\subsection{Persistent scale-space kernel} +The other possibility is to define feature maps \emph{implicitly} by +building kernels on persistence diagrams. Such a kernel allows to use +a wide range of kernel-based machine-learning methods. -\cite{reininghaus_stable_2015,kwitt_statistical_2015} +Let us recall the general framework of kernel +methods~\cite{muandet_kernel_2017, sejdinovic_advanced_2018}. -\subsection{Persistence weighted-Gaussian kernel} +\begin{defn}[kernel] + A function $k:X\times X \mapsto \mathbb{R}_+$ on a non-empty set $X$ + is a \emph{kernel} if there exist a Hilbert space $\mathcal{H}$ and + a map $\phi:X\mapsto\mathcal{H}$ such that + \[ \forall x, y \in X,\; k(x,y) = {\langle \phi(x), \phi(y) + \rangle}_{\mathcal{H}}. \] -\cite{kusano_kernel_2017} + The Hilbert space $\mathcal{H}$ is called the \emph{feature space} + and the function $\phi$ is called the \emph{feature map}. +\end{defn} -\subsection{Sliced Wasserstein kernel} +As inner products are positive definite, so are kernels, since they +are inner products on feature maps. -\cite{carriere_sliced_2017} +\begin{defn}[Reproducing kernel] + Let $\mathcal{H}$ be a Hilbert space of functions from a non-empty + set $X$ to $\mathbb{R}$. A function $k:X\mapsto\mathbb{R}$ is called + a \emph{reproducing kernel} of $\mathcal{H}$ if it satisfies: + \begin{itemize} + \item $\forall x\in X,\; k(\cdot,x)\in\mathcal{H}$, + \item + $\forall x\in X, \forall f\in\mathcal{H},\; {\langle f, + k(\cdot,x)\rangle}_{\mathcal{H}} = f(x)$. + \end{itemize} +\end{defn} + +Note that every reproducing kernel is a kernel, with feature space +$\mathcal{H}$ and feature map $\phi:x \mapsto k(\cdot,x)$. In this +case, $\phi$ is called the \emph{canonical feature map}: the features +are not explicited as vectors of $\mathbb{R}^n$, but as functions on +$X$. + +If $\mathcal{H}$ has a reproducing kernel, it is called a +\emph{reproducible kernel Hilbert space} (RKHS). The important result +here is the \emph{Moore-Aronszajn + theorem}~\cite{berlinet_reproducing_2011}: for every positive +definite function $k$, there exists a unique RKHS with kernel $k$. + +We can now build a feature space with a Hilbert structure without +defining explicitly the feature map. Defining a kernel, i.e.\ any +positive definite function, on persistence diagrams is enough to +guarantee the existence of a unique RKHS with the adequate structure +to perform machine-learning tasks. + +The following sections will define some relevant kernels. + +\subsection{Sliced Wasserstein kernel}% +\label{sec:swk} + +The sliced Wasserstein kernel is a new kernel on persistence diagrams +introduced by Carrière et al.\ in~\cite{carriere_sliced_2017}. The +general idea is to intersect the plane by lines going through the +origin, and projecting the points of the persistence diagrams onto +these lines, computing the distance between the diagrams as the +distance between measures on the real line. These distances are then +integrated over all the possible lines passing through the origin. + +The formal definition (taken from~\cite{carriere_sliced_2017}) relies +on the \emph{1-Wasserstein distance} between measures on~$\mathbb{R}$. + +\begin{defn}[1-Wasserstein distance] + Lt $\mu$ and $\nu$ be two non-negative measures on $\mathbb{R}$ such + that $\mu(\mathbb{R}) = \nu(\mathbb{R})$. The 1-Wasserstein distance + between $\mu$ and $\nu$ is + \[ \mathcal{W}(\mu, \nu) = \inf_{f} \int_{\mathcal{R}} f(x) \left[ + \mu(\diff x) - \nu(\diff x) \right], \] + where $f$ is 1-Lipschitz. +\end{defn} + +One can now define formally the sliced Wasserstein kernel. + +\begin{defn}[Sliced Wasserstein kernel] + Let $\mathbb{S}_1$ be the $L_2$-distance sphere in + $\mathbb{R}^2$. Given $\theta\in\mathbb{S}_1$ let $L(\theta)$ be the + line $\{\lambda\theta : \lambda\in\mathbb{R}\}$, and $\pi_\theta$ + the orthogonal projection onto $L(\theta)$. Let $\pi_\Delta$ be the + orthogonal projection on the diagonal. + + Let $D_1$ and $D_2$ be two persistence diagrams, and let + \[\mu_1^\theta = \sum_{p\in D_1} \delta_{\pi_\theta(p)} \qquad\text{and}\qquad + \mu_{1\Delta}^\theta = \sum_{p\in D_1} + \delta_{\pi_\theta\circ\pi_\Delta(p)},\] and similarly for + $\mu_2^\theta$ and $\mu_{2\Delta}^\theta$. + + The sliced Wasserstein distance is defined as + \[ SW(D_1, D_2) = \frac{1}{2\pi} \int_{\mathbb{S_1}} + \mathcal{W}(\mu_1^\theta + \mu_{2\Delta}^\theta,\; \mu_2^\theta + + \mu_{1\Delta}^\theta) \diff\theta. \] +\end{defn} + +One can show that $SW$ is negative +definite~\cite{carriere_sliced_2017}. The function $k_{SW}$ defined as +\[ k_{SW}(D_1, D_2) = \exp\left(-\frac{SW(D_1,D_2)}{2\sigma^2}\right) \] +is therefore a valid kernel, called the \emph{sliced Wasserstein + kernel}. + +\paragraph{Stability} + +It can be shown that the sliced Wasserstein kernel is +\emph{equivalent} to the 1-Wasserstein distance between persistence +diagrams (\autoref{defn:wasserstein-dist}). (For a definition of +metric equivalence, see~\autoref{cha:topology}.) + +\paragraph{Approximate computation} + +In practice, $k_{SW}$ can be approximated by sampling $M$ directions +between $-\pi/2$ and $\pi/2$. For each direction $\theta_i$ and for +each persistence diagram $D$, one computes the scalar products between +the points of the diagram and $\theta_i$, and sorts them into a vector +$V_{\theta_i}(D)$. The $L_1$-distance between the vectors +corresponding to each diagram is then averaged over the samples +directions: +\[ SW_M(D_1, D_2) = \frac{1}{M} \sum_{i=1}^M {\lVert V_{\theta_i}(D_1) + - V_{\theta_i}(D_2) \rVert}_1. \] + +The complete approximate computation is detailed +in~\autoref{algo:swk}. It has a complexity of +$\mathcal{O}(MN\log(N))$, where $N$ is an upper bound on the +cardinality of the persistence diagrams. + +\begin{algorithm}[ht] + \caption{Approximate computation of the sliced Wasserstein kernel.}\label{algo:swk} + \DontPrintSemicolon% + \KwIn{$D_1 = \{p_1^1,\ldots,p_{N_1}^1\}, D_2 = \{p_1^2,\ldots,p_{N_1}^2\}, M$} + \KwOut{$SW$} + Add $\pi_\Delta(D_1)$ to $D_2$ and vice-versa\; + $SW \leftarrow 0$\; + $\theta \leftarrow -\pi/2$\; + $s \leftarrow \pi/M$\; + \For{$i \leftarrow 1$\KwTo$M$}{ + Store the products $\langle p_k^1, \theta \rangle$ in an array $V_1$\; + Store the products $\langle p_k^2, \theta \rangle$ in an array $V_2$\; + Sort $V_1$ and $V_2$ in ascending order\; + $SW \leftarrow SW + s {\lVert V_1 - V_2 \rVert}_1$\; + $\theta \leftarrow \theta + s$\; + } + $SW \leftarrow SW/\pi$\: +\end{algorithm} + +\subsection{Persistence scale-space kernel}% +\label{sec:pers-scale-space} + +The persistent scale-space kernel +(PSSK)~\cite{reininghaus_stable_2015,kwitt_statistical_2015} is +another kernel on persistence diagrams. The following overview is +summarised from~\cite{reininghaus_stable_2015}. The general idea is to +represent a diagram $D$ as a sum of Dirac deltas centred on each point +of $D$. This representation is a natural projection onto the sapce of +functionals, which has a Hilbert structure. + +However, this representation does not take into account the distance +of the points of $D$ to the diagonal. This is important since points +closed to the diagonal represent short-lived features, and are +therefore more likely to be noise. Do take this into account, the sum +of Dirac deltas is taken as the initial condition of a heat diffusion +on the half-plane above the diagonal, with a null boundary condition +on the diagonal itself. + +This leads to the definition of the embedding as the solution of +partial differential equation, which admit an explicit solution in the +form of a positive definite kernel between persistence diagrams. This +kernel also depends on a scale parameter, which allows to control the +robustness of the embedding to noise. + +This kernel also comes with stability guarantees, as it is +Lipschitz-continuous with respect to the 1-Wasserstein distance. It is +also fast, as the distance between two diagrams $D_1$ and $D_2$ can be +computed in $\mathcal{O}(\lvert D_1 \rvert \lvert D_2 \rvert)$, where +$\lvert D \rvert$ is the number of points in the diagram, or +approximated in $\mathcal{O}(\lvert D_1 \rvert + \lvert D_2 \rvert)$ +with bounded error. In practice, empirical tests show that the +persistence scale-space kernel significantly outperforms the +persistence landscapes in shape classification +tasks~\cite{reininghaus_stable_2015}. + +\subsection{Persistence weighted Gaussian kernel}% +\label{sec:pers-weight-gauss} + +The persistence weighted Gaussian kernel +(PWGK)~\cite{kusano_kernel_2017} is actually a family of kernels on +persistence diagrams. Given a diagram $D$, one can define a measure +$\mu_D^w := \sum_{x\in D} w(x) \delta_x$, where $\delta_x$ is the +Dirac delta centred on $x$. The weight function $w$ can be chosen to +give more weight to points farther from the diagonal. One example is +$w(x) := \arctan\left(C {(\mathrm{death}(x) - + \mathrm{birth}(x))}^p\right)$, with $C>0$ and $p\in\mathbb{N}^*$. + +Then, given a kernel $k$ and the corresponding RKHS $\mathcal{H}_k$, +\[ \mu_D^w \mapsto \sum_{x\in D} w(x) k(\cdot, x) \] is an embedding +of $\mu_D^w$ in $\mathcal{H}_k$. The persistence weighted gaussian +kernel is obtained by choosing $k$ as the Gaussian kernel +$k_G(x,y) := \exp\left(-\frac{{\lVert x-y \rVert}^2}{2\sigma^2} +\right)$. + +The PWGK is stable with respect to the bottleneck +distance~\cite{kusano_kernel_2017}, and allows for efficient +computation. If the persistent diagrams contain at most $n$ points, +computation of the kernel involves $\mathcal{O}(n^2)$ evaluations of +the kernel $k$. Similarly to the PSSK, an approximation is possible in +$\mathcal{O}(n)$. + +Experimental results on shape classification with SVMs show a +significant improvement in accuracy over the PSSk, persistence images, +and persistent landscapes~\cite{kusano_kernel_2017}. \section{Comparison}% \label{sec:comparison} +Every vectorization exposed in the previous sections are injective and +stable with respect to some distance in the space of persistence +diagrams. None of them, however, are surjective, and no explicit +inverse exists. + +Only one of these methods preserves the metric on the space of +persistence diagrams: the sliced Wasserstein kernel, due to its +equivalence to the 1-Wasserstein distance, as mentioned +in~\autoref{sec:swk}. As such, it is considered as the +state-of-the-art in kernel embeddings of persistence diagrams. + +There are two broad classes of applications that require different +kinds of vectorization methods. On the one hand, if one needs to go +back from the feature space to the diagram space, the best bet is an +embedding that preserves distances, such as the sliced Wasserstein +kernels, or has strong stability guarantees, such as the persistent +weighted Gaussian kernel. These embeddings are best for distance-based +methods, such as multidimensional scaling or nearest neighbours +algorithms. + +On the other hand, getting insights from individual points of a +diagram, in order to recover information about individual topological +features (such as cycles, holes, or voids), is a much harder, less +well-studied problem. For instance, to recover the topological +features of the mean of persistence diagrams, one would need to fit +one of the vectorization methods on the mean. For this, persistence +landscapes or images seem better suited. + +This project focuses on clustering of networks. As such, conservation +of the metric and stability is extremely important. Due to the +theoretical guarantees, we will focus on the sliced Wasserstein +kernel, which is also significantly easier to implement in its +approximate version than the PSSK (which uses random Fourier +features~\cite{reininghaus_stable_2015}) and the PWGK (which uses the +fast Gauss transform~\cite{kusano_kernel_2017}). + +\chapter{Temporal partitioning of networks}% +\label{cha:temp-part-netw} + +\section{Problem statement}% +\label{sec:problem-description} + +\subsection{Data}% +\label{sec:data} + +Temporal networks represent an active and recent area of research. The +additional dimension adds complexity to the study of graphs. As such, +many methods that work well with graphs fail in the context of +temporal networks. + +Temporal networks are much more difficult to visualize, which makes it +harder to uncover patterns +directly~\cite{holme_temporal_2012}. Moreover, there are many issues +in data collection. Complex interaction networks where each edge can +be either present or absent at each time step grow exponentially in +size with the number of nodes and the total data collection +time~\cite{holme_temporal_2012}. Empirical temporal networks also tend +to exhibit oversampling and noise, due to the nature of the +measurements. For instance, proximity networks can record an +interaction between two individuals if they walk close to each other +without actually interacting. New advances try to take into account +these limitations of data collection~\cite{sulo_meaningful_2010, + newman_network_2018}. + +In this study, we will consider temporal networks with \emph{contact} +interactions. In this context, interactions between nodes are supposed +to have a duration of~0, and \emph{oversampling} is used to represent +a long interaction. For instance, in a network sampled every +5~seconds, an interaction lasting for 30~seconds will be recorded in 6 +consecutive time steps. + +\subsection{Sliding windows}% +\label{sec:sliding-windows} + +One possible solution to the study temporal networks is a partitioning +of the time scale using \emph{sliding windows}. + +\begin{defn}[Temporal partitioning]\label{defn:partitioning} + Let $G = (V, E, \mathcal{T}, \rho)$ a temporal network, and let + $C = (c_1,\ldots,c_n)$ be a cover of $\mathcal{T}$ by non-empty + intervals of $\mathbb{N}$. + + Then the sequence of temporal networks $(G_1,\ldots,G_n)$, where + $G_i = (V, E, c_i, \rho_i)$ and + $\rho_i(e, t) = \rho(e, t)\mathbb{1}_{t\in c_i}$, is a + \emph{temporal partitioning} of $G$. + + The partitioning is \emph{uniform} if all intervals of $C$ have the + same length. This length is called the \emph{temporal resolution} of + the partitioning. +\end{defn} + +In this project, we will only consider uniform partitioning of a +finite temporal domain, where the intersection of two consecutive +intervals have the same length. This intersection length is called the +\emph{overlap}. + +\begin{figure}[!ht] + \centering + \begin{tikzpicture} + \def\t{2} + \def\s{.5} + \foreach \x in {0,...,4} + \draw[thick,|-|] ({2*\x*(\t - \s)},0) -- ({2*\x*(\t - \s) + \t},0); + \foreach \x in {0,...,3} + \draw[thick,|-|] ({2*\x*(\t - \s) + \t - \s},.4) -- ({2*\x*(\t - \s) + 2*\t - \s},.4); + \draw[<->] (0,-.2) -- (\t,-.2) node [below,midway] {$\Delta t$}; + \draw[<->] ({\t - \s}, .6) -- (\t, .6) node [above,midway] {$s$}; + % \draw[thick,->] (0,-1) -- ({8*(\t - \s) + \t},-1) node [above] {$t$}; + \end{tikzpicture} + \caption{Uniform temporal partitioning with resolution $\Delta t$ and overlap $s$.}% + \label{fig:partitioning} +\end{figure} + +The choice of temporal resolution and overlap have a significant +effect on the results of the analysis~\cite{ribeiro_quantifying_2013, + krings_effects_2012, sulo_meaningful_2010}. Different tasks may +require specific parameters. A large resolution can overlook a +significant pattern, while small overlap may cut through significant +features, divided between two consecutive intervals. + +\subsection{Classification}% +\label{sec:classification} + +After partitioning the temporal network, it is possible to run any +kind of classification task on the resulting sequence of subnetworks. + +If labels are available on each subnetwork, it is possible to run some +supervised learning tasks. In the more common case of unsupervised +learning, there are many possibilities, including the clustering of +all subnetworks, or the detection of change points, where the +structure of the network change +fundamentally~\cite{peel_detecting_2014}. + +In this dissertation, we focus on unsupervised clustering of the +subnetworks in order to detect periodicity in the original temporal +network. Most machine-learning algorithms cannot take temporal +networks directly as inputs. It is thus necessary to \emph{vectorize} +these networks, i.e.\ to project them onto a metric space with a +structure suitable to the algorithm used. For instance, one could use +traditional statistical summaries of networks +(\autoref{sec:network-statistics}), or the topological methods and +their vectorizations discussed in the previous chapters. + +The choice of vectorization depends on the choice of the clustering +algorithm itself. Some machine-learning techniques, such as support +vector machines, require a Hilbert structure on the input +space~\cite{carriere_sliced_2017, hastie_elements_2009}, while some, +like $k$-nearest neighbours or agglomerative clustering, only require +a metric space~\cite{hastie_elements_2009}. The feature space will +therefore restrict the set of clustering algorithms available. + +\subsection{Applications}% +\label{sec:applications} + +The persistent homology pipeline can be used to determine different +properties of temporal networks. This study focuses on determining the +\emph{periodicity} of a temporal network. By clustering the +subnetworks obtained by partitioning the temporal domain into sliding +windows, it is possible to determine if a temporal network is periodic +in its topological structure, and if so, to estimate its +period. + +\section{The analysis pipeline}% +\label{sec:analysis-pipeline} + +\subsection{General overview}% +\label{sec:general-overview} + +\begin{figure}[p] + \caption[Overview of the analysis pipeline.]{Overview of the + analysis pipeline. New approaches introduced in this study are + highlighted in \emph{italics}.}% + \label{fig:pipeline} + \centering + %\footnotesize + \begin{tikzpicture}[block_left/.style={rectangle,draw=black,thick,fill=white,text width=4cm,text centered,inner sep=6pt}, + block_right/.style={rectangle,draw=black,thick,fill=white,text width=8cm,text ragged,inner sep=6pt}, + line/.style={draw,thick,-latex',shorten >=0pt}, + dashed_line/.style={draw,thick,dashed,-latex',shorten >=0pt}] + \matrix [column sep=2cm,row sep=1cm] { + \node {\Large\textbf{General approach}}; + & \node {\Large\textbf{Specific pipeline}}; \\ + \node (dataset)[block_left] {Dataset}; + & \node (dataset_r)[block_right] { + Data sources + \begin{itemize} + \item Generative model (\ref{sec:gener-model-peri}) + \begin{itemize} + \item Erdős-Rényi, Watts-Strogatz models + \item periodic distribution of interactions + \end{itemize} + \item Social networks data (\ref{sec:datasets}) + \end{itemize} + }; \\ + \node (representation)[block_left] {Data representation}; + & \node (representation_r)[block_right]{ + Temporal networks + \begin{itemize} + \item Definition (\ref{sec:defin-basic-prop}) + \item Representation (\ref{sec:data-representation}) + \end{itemize} + }; \\ + \node (processing)[block_left] {Data processing}; + & \node (processing_r)[block_right] { + Temporal partitioning \emph{(standard)} (\ref{sec:sliding-windows-1})\\ + \emph{Novelty: Clustering of time windows} + }; \\ + \node (analysis)[block_left] {Data analysis}; + & \node (analysis_r)[block_right] { + Topological tools (\ref{sec:topological-analysis})\\ + \emph{Novelty: application of TDA to temporal networks}\\ + \emph{Novelty: comparison between WRCF and zigzag persistence for networks} + }; \\ + \node (interpretation)[block_left] {Interpretation}; + & \node (interpretation_r)[block_right] { + Clustering (\ref{sec:clustering}) + \begin{itemize} + \item Distance matrix + \item[] \emph{Novelty: comparison between bottleneck distance and SW kernel} + \item Hierarchical clustering + \end{itemize} + }; \\ + }; + + \begin{scope}[every path/.style=line] + \path (dataset) -- (representation); + \path (representation) -- (processing); + \path (processing) -- (analysis); + \path (analysis) -- (interpretation); + \end{scope} + \begin{scope}[every path/.style=dashed_line] + \path (dataset) -- (dataset_r); + \path (representation) -- (representation_r); + \path (processing) -- (processing_r); + \path (analysis) -- (analysis_r); + \path (interpretation) -- (interpretation_r); + \end{scope} + \end{tikzpicture} +\end{figure} + +The analysis pipeline consists in several steps: +\begin{enumerate} +\item Load the data: temporal networks are often distributed as + \emph{interaction lists}. In these files, each line consists of two + nodes and a timestamp, and thus represents one contact + interaction. One can reconstruct the temporal network by extracting + all timestamps of a given edge and adding them as an edge + property. It is then easy to extract a subnetwork within a specific + time interval. +\item Interaction networks are sometimes directed. In these cases, it + is necessary to transform the network into an undirected one, as + most method (particularly topological methods, such as WRCF and + zigzag persistence) only work on undirected networks. +\item Using the methods discussed in~\autoref{sec:sliding-windows-1}, + the temporal domain is segmented into sliding windows and a list of + subnetworks can be generated. +\item Features are extracted from each subnetwork. These features can + be constructed from different kinds of persistent homology on + networks, as discussed in~\autoref{sec:topological-analysis}. +\item Depending on the methods used, the feature space is equipped + with a metric that can make it a Hilbert space or a simple metric + space. In any case, a distance matrix representing pairwise + distances between each subnetwork is computed. +\item Hierarchical clustering is applied to the distance matrix. +\end{enumerate} + +The whole analysis pipeline is summarised in~\autoref{fig:pipeline}. + +\subsection{Data representation}% +\label{sec:data-representation} + +The data is represented in the algorithms as multigraphs. Each edge is +associated to a timestamp (an integer). Two nodes can be linked by +multiple edges, each one of them representing a time at which the edge +is present. + +This representation allows for easy filtering, as one can extract a +temporal network in a given time interval by keeping only the edges +whose timestamp is included in the interval. One can also build the +underlying aggregated graph by ``collapsing'' multiple edges into a +single one. + +It is important to note that the nodes of the network are completely +static and always present. This follows the temporal networks model +adopted in~\autoref{defn:temp-net}. + +\subsection{Sliding windows}% +\label{sec:sliding-windows-1} + +As mentioned in~\autoref{sec:sliding-windows}, we consider temporal +networks whose temporal domain is a finite interval of $\mathbb{N}$. + +For a temporal resolution $\Delta t$ and an overlap $s$, we compute +the temporal partitioning as follows. +\begin{enumerate} +\item Compute the length of the temporal domain $\mathcal{T}$. +\item Segment it into $N$ sliding windows of length $\Delta t$ with an + overlap $s$. +\item Each subnetwork in the sequence contains only the interactions + appearing during the corresponding sliding window. +\end{enumerate} + +\begin{algorithm}[ht] + \caption{Temporal partitioning of network with sliding + windows.}\label{algo:partitioning} + \DontPrintSemicolon% + \SetKwData{Res}{res} + \SetKwData{Times}{times} + \SetKwData{WinLength}{window\_length} + \SetKwData{Windows}{windows} + \KwIn{Graph $G$, resolution \Res} + \KwOut{List of subnetworks \Windows} + \Times$\leftarrow$ list of timestamps in $G$\; + $\WinLength\leftarrow\Res\times (\max(\Times) - \min(\Times))$\; + \For{$i \leftarrow 0$ \KwTo$1/\Res - 1$}{ + \Windows[$i$] $\leftarrow$ subnetwork of $G$ containing all nodes, and + edges whose timestamp is in + $\left[ \min(\Times) + \WinLength\times i, \min(\Times) + + \WinLength\times(i+1) \right]$\; + } +\end{algorithm} + +\subsection{Topological analysis}% +\label{sec:topological-analysis} + +The major novelty in this analysis is to introduce topological +features in temporal network analysis. The idea is that the techniques +introduced in~\autoref{cha:tda-ph} will reveal additional structure in +networks that is not captured by traditional methods, and is relevant +for detecting periodicity or other important properties of temporal +networks. + +Here, two different approaches are presented and compared. One is +focusing on the topology of the aggregated graph, using weight-rank +clique filtration, while the other leverages the temporal dimension by +using the more recent advances in generalized persistence, +specifically zigzag persistence. + +\subsubsection{Aggregated graph persistence homology}% +\label{sec:aggr-graph-pers} + +The first possibility to introduce topological features into the +feature map is to use weight-rank clique filtration +(\autoref{sec:pers-homol-netw}) on the aggregated static graphs. + +For this, we associate to each edge in the network a weight +corresponding to the number of time steps in which it is present. For +an edge $e$ and a time interval $c_i$ (keeping the notations +of~\autoref{defn:partitioning}), the weight associated to $e$ is +\[ w(e) = \sum_{t\in c_i} \rho(e,t). \] + +The resulting graph is called the \emph{aggregated graph} of the +temporal network on the time interval $c_i$. This graph being +weighted, it is possible to compute persistence diagrams using +weight-rank clique filtration (the algorithm is exposed +in~\autoref{sec:pers-homol-netw}). + +% \subsubsection{Traditional network statistics}% +% \label{sec:trad-netw-stat} + +% Network statistics (\autoref{sec:network-statistics}) can be used as +% feature maps. First, temporal networks are transformed into static +% graphs by keeping all edges that appear at least once in the network. + +% One can then combine different centrality measures, average degree, +% average shortest path length, and others statistical summaries into a +% vector which is used as a feature vector. Equipped with the Euclidean +% distance, this feature space form a metric space suitable for +% machine-learning tasks. + +\subsubsection{Zigzag persistence}% +\label{sec:zigzag-persistence-1} + +The main drawback of WRCF persistence is the loss of any kind of +temporal information in the network. Three nodes can be detected as +being very close to one another even though their contacts might have +been in separate time steps. We can avoid aggregating the temporal +networks by using \emph{generalised persistence}, specifically zigzag +persistence as exposed in~\autoref{sec:zigzag-persistence}. + +In practice, zigzag persistence is more computationally expensive than +WRCF persistence~\cite{carlsson_zigzag_2009}, and leads to lower +number of topological features at every dimension. Aggregating +networks tend to artificially create a lot of cliques that do not +appear in the original temporal network. + +To compute zigzag persistence, the algorithm needs the \emph{maximal + simplicial complex}, i.e.\ the union of all simplicial complexes in +the sequence. In the case of temporal networks, this is the set of +maximal cliques in the aggregated graph. Zigzag persistence can then +be computed from the list of times when each simplex enters or leaves +the maximal filtration. The following algorithm determines these +times: +\begin{enumerate} +\item Determine the maximal simplicial complex by computing the + cliques in the aggregated graph. +\item For each time step $t$: + \begin{itemize} + \item Keep only the edges present at this time step (i.e.\ the edges + $e$ such that $\rho(e, t) = 1$). + \item Compute all the cliques in this network. + \end{itemize} +\item For each clique in the maximal simplicial complex, determine + where it is present and where it is absent in the sequence of lists + of cliques. +\item Finally, determine the transition times in the presence arrays. +\end{enumerate} + +This computation can be quite costly for large networks, even before +starting the main zigzag algorithm. Clique-finding is indeed an +NP-complete problem~\cite{karp_reducibility_2010}. This is thus by far +the most computationally expensive step of the analysis pipeline, and +is also more expensive than WRCF persistence. + +\subsection{Clustering}% +\label{sec:clustering} + +\subsubsection{Distance matrix}% +\label{sec:distance-matrix} + +In order to cluster the subnetworks obtained by temporal partitioning +of the original network, one needs to introduce a notion of distance +between the topological features. Since the output of the previous +step in the analysis pipeline take the form of persistence diagrams, +two options are possible: a standard measure of distance between +diagrams (see~\autoref{sec:topol-summ}), or one of the vectorization +or kernel-based methods exposed in~\autoref{cha:pers-homol-mach}. + +One of the main contributions of this study is to compare the +performance of the bottleneck distance (\autoref{defn:bottleneck}) and +of the sliced Wasserstein kernel (\autoref{sec:swk}) in the context of +network clustering. + +A distance matrix is obtained by computing pairwise distances between +each pair of subnetworks obtained during the temporal partitioning +step. One important remark is that the distances considered compute +distances between persistence diagrams. However, persistence homology +returns a \emph{sequence} of such persistence diagrams for each +subnetwork, each diagram in the sequence corresponding to topological +features of a specific dimension. For the purposes of clustering, +0-dimensional features are not extremely interesting since they +correspond to connected components, and 2 or 3-dimensional diagrams +are often nearly empty except for very large subnetworks. It is +therefore appropriate to restrict our analysis to 1-dimensional +diagrams, which represent a good compromise. This is consistent with +what has been done for point cloud data +classification~\cite{carriere_sliced_2017}. + +The bottleneck distance gives the space of persistence diagram a +metric-space structure. Meanwhile, the sliced Wasserstein kernel gives +the space a structure of a Hilbert space, which can be required +for several machine-learning techniques, such as support-vector +machines (SVMs) or principal components analysis (PCA). + +For the implementation, we use the approximate computation of the +sliced Wasserstein kernel (\autoref{algo:swk}) sampled along 10 +directions, which is actually faster in practice than the computation +of the bottleneck distance. For the computation of the bottleneck +distance, the diagram points that go to infinity are +excluded. According to the definition of the bottleneck distance, if +two diagrams do not have the same number of infinite points, the +distance is automatically infinity, which does not work well in +clustering algorithms. Moreover, this does not interfere with the +comparison between the bottleneck distance and the sliced Wasserstein +kernel, since infinite points are ignored by the kernel anyway. + +\subsubsection{Hierarchical clustering}% +\label{sec:hier-clust} + +To simplify the interpretation of the analysis and the comparison +between the different approaches, the clustering algorithm used is +\emph{hierarchical clustering}~\cite{hastie_elements_2009}. + +The main advantage is that it does not require knowing in advance the +number of clusters that one is looking for. The only input is the +dissimilarity matrix, obtained from a single metric. It is necessary +here to use an algorithm that does not require the observations +themselves, as in this case they take the form of a persistence +diagram instead of a numeric vector. Moreover, kernel-based methods +are not applicable to the bottleneck distance since it does not confer +a Hilbert structure to the space of persistence diagram. By contrast, +hierarchical clustering only requires a valid measure of distance. + +The hierarchical representation (or \emph{dendrogram}) is also +especially useful in the context of periodicity detection, since +periodicity can appear at various levels of the hierarchy. + +Hierarchical clustering is performed in a bottom-up way, also called +\emph{agglomerative clustering}. Starting from the distance matrix, +with each observation in its own cluster, the algorithm merges rows +and columns at each step of the clustering, updating the distances +between the new clusters. To do that, it needs to compute the distance +between two clusters. Several approaches are possible to compute the +distance between two clusters $A$ and $B$ using a metric $d$: +\begin{itemize} +\item Complete linkage: the distance between $A$ and $B$ is the + maximum distance between their elements + \[ \max\left\{ d(x,y) : x\in A, y\in B \right\} \] +\item Single linkage: using the minimum distance + \[ \min\left\{ d(x,y) : x\in A, y\in B \right\} \] +\item Average linkage: using the mean distance between the elements + \[ \frac{1}{|A| |B|} \sum_{x\in A} \sum_{y\in B} d(x,y). \] +\end{itemize} + +The implementation used is taken from the library +Scikit-Learn~\cite{pedregosa_scikit-learn:_2011}. + +\chapter{Results and Discussion}% +\label{cha:results-discussion} + +\section{Data}% +\label{sec:data-1} + +\subsection{Generative model for periodic temporal networks}% +\label{sec:gener-model-peri} + +In order to detect periodicity, one can generate a random temporal +network with a periodic structure. + +We first build a random Erdős-Rényi graph. Starting from this base +graph, we generate a temporal stream for each edge independently. This +generative model is inspired by previous work on periodic temporal +networks~\cite{price-wright_topological_2015}. + +For each edge, we generate a sequence of times in a predefined time +range $T$. For this, we choose uniformly at random a number of +interactions $n$ in $[0, T/2]$. We then generate at random a sequence +of $n$ times in $[0, T]$ from a density +\[ f(t) = \sin(f t) + 1, \] where $f$ is the frequency. The times are +then sorted. + +\begin{figure}[ht] + \centering + \begin{subfigure}[b]{0.2\linewidth} + \begin{tikzpicture} + \clip (0,0) rectangle (4.0,4.0); + \Vertex[x=1.408,y=1.635,size=0.3,color=blue,opacity=0.7]{0} + \Vertex[x=1.089,y=2.415,size=0.3,color=blue,opacity=0.7]{1} + \Vertex[x=2.021,y=1.976,size=0.3,color=blue,opacity=0.7]{2} + \Vertex[x=1.984,y=3.000,size=0.3,color=blue,opacity=0.7]{3} + \Vertex[x=2.576,y=2.398,size=0.3,color=blue,opacity=0.7]{4} + \Vertex[x=2.911,y=1.593,size=0.3,color=blue,opacity=0.7]{5} + \Vertex[x=1.994,y=1.000,size=0.3,color=blue,opacity=0.7]{6} + \Edge[](1)(3) + \Edge[](3)(4) + \Edge[](3)(4) + \Edge[](0)(5) + \Edge[](0)(5) + \Edge[](2)(5) + \Edge[](4)(5) + \Edge[](0)(6) + \Edge[](1)(6) + \Edge[](5)(6) + \end{tikzpicture} + \end{subfigure} + \begin{subfigure}[b]{0.2\linewidth} + \begin{tikzpicture} + \clip (0,0) rectangle (4.0,4.0); + \Vertex[x=1.408,y=1.635,size=0.3,color=blue,opacity=0.7]{0} + \Vertex[x=1.089,y=2.415,size=0.3,color=blue,opacity=0.7]{1} + \Vertex[x=2.021,y=1.976,size=0.3,color=blue,opacity=0.7]{2} + \Vertex[x=1.984,y=3.000,size=0.3,color=blue,opacity=0.7]{3} + \Vertex[x=2.576,y=2.398,size=0.3,color=blue,opacity=0.7]{4} + \Vertex[x=2.911,y=1.593,size=0.3,color=blue,opacity=0.7]{5} + \Vertex[x=1.994,y=1.000,size=0.3,color=blue,opacity=0.7]{6} + \Edge[](1)(2) + \Edge[](0)(3) + \Edge[](2)(4) + \Edge[](2)(6) + \end{tikzpicture} + \end{subfigure} + \begin{subfigure}[b]{0.2\linewidth} + \begin{tikzpicture} + \clip (0,0) rectangle (4.0,4.0); + \Vertex[x=1.408,y=1.635,size=0.3,color=blue,opacity=0.7]{0} + \Vertex[x=1.089,y=2.415,size=0.3,color=blue,opacity=0.7]{1} + \Vertex[x=2.021,y=1.976,size=0.3,color=blue,opacity=0.7]{2} + \Vertex[x=1.984,y=3.000,size=0.3,color=blue,opacity=0.7]{3} + \Vertex[x=2.576,y=2.398,size=0.3,color=blue,opacity=0.7]{4} + \Vertex[x=2.911,y=1.593,size=0.3,color=blue,opacity=0.7]{5} + \Vertex[x=1.994,y=1.000,size=0.3,color=blue,opacity=0.7]{6} + \Edge[](0)(2) + \Edge[](0)(2) + \Edge[](1)(4) + \Edge[](2)(5) + \Edge[](4)(5) + \Edge[](0)(6) + \Edge[](2)(6) + \Edge[](4)(6) + \end{tikzpicture} + \end{subfigure} + \begin{subfigure}[b]{0.2\linewidth} + \begin{tikzpicture} + \clip (0,0) rectangle (4.0,4.0); + \Vertex[x=1.408,y=1.635,size=0.3,color=blue,opacity=0.7]{0} + \Vertex[x=1.089,y=2.415,size=0.3,color=blue,opacity=0.7]{1} + \Vertex[x=2.021,y=1.976,size=0.3,color=blue,opacity=0.7]{2} + \Vertex[x=1.984,y=3.000,size=0.3,color=blue,opacity=0.7]{3} + \Vertex[x=2.576,y=2.398,size=0.3,color=blue,opacity=0.7]{4} + \Vertex[x=2.911,y=1.593,size=0.3,color=blue,opacity=0.7]{5} + \Vertex[x=1.994,y=1.000,size=0.3,color=blue,opacity=0.7]{6} + \Edge[](0)(1) + \Edge[](1)(2) + \Edge[](2)(3) + \Edge[](2)(3) + \Edge[](0)(4) + \Edge[](0)(4) + \Edge[](1)(4) + \Edge[](2)(4) + \Edge[](3)(5) + \Edge[](4)(6) + \Edge[](5)(6) + \end{tikzpicture} + \end{subfigure} + % \begin{subfigure}[b]{0.2\linewidth} + % \begin{tikzpicture} + % \clip (0,0) rectangle (4.0,4.0); + % \Vertex[x=1.408,y=1.635,size=0.3,color=blue,opacity=0.7]{0} + % \Vertex[x=1.089,y=2.415,size=0.3,color=blue,opacity=0.7]{1} + % \Vertex[x=2.021,y=1.976,size=0.3,color=blue,opacity=0.7]{2} + % \Vertex[x=1.984,y=3.000,size=0.3,color=blue,opacity=0.7]{3} + % \Vertex[x=2.576,y=2.398,size=0.3,color=blue,opacity=0.7]{4} + % \Vertex[x=2.911,y=1.593,size=0.3,color=blue,opacity=0.7]{5} + % \Vertex[x=1.994,y=1.000,size=0.3,color=blue,opacity=0.7]{6} + % \Edge[](0)(1) + % \Edge[](0)(3) + % \Edge[](1)(3) + % \Edge[](3)(5) + % \Edge[](1)(6) + % \end{tikzpicture} + % \end{subfigure} + \caption{Example of a random temporal network generated + by~\autoref{algo:generative-model}.}% + \label{fig:random-example} +\end{figure} + +\begin{algorithm}[ht] + \caption{Random temporal network + generation.}\label{algo:generative-model} + \DontPrintSemicolon% + \SetKwData{Basegraph}{basegraph} + \SetKwData{TimeRange}{time\_range} + \SetKwData{Nodes}{nodes} + \SetKwData{EdgeProb}{edge\_prob} + \SetKwData{Frequency}{frequency} + \SetKwData{Network}{network} + \SetKwData{Times}{times} + \KwIn{\Nodes, \EdgeProb, \TimeRange, \Frequency} + \KwOut{\Network} + $\Basegraph\leftarrow \mathrm{ErdősRényi}(\Nodes, \EdgeProb)$\; + $\Network\leftarrow$ network with no edges and the vertices of \Basegraph\; + \For{$e\in \Basegraph.\mathrm{edges}$}{ + $\Times\leftarrow \mathrm{random\_edge\_presences}(\TimeRange, \Frequency)$\; + \For{$t\in\Times$}{Add $(e.\mathrm{source}, e.\mathrm{target}, t)$ to \Network} + } +\end{algorithm} + +The complete method to generate a random network is summarised +in~\autoref{algo:generative-model}. The function +\texttt{random\_edge\_presences} returns a sequence of periodic +times. An example of a small random network can be found +on~\autoref{fig:random-example}. + +\subsection{Datasets}% +\label{sec:datasets} + +The SocioPatterns dataset~\cite{isella_whats_2011} has been collected +during the \textsc{infectious} exhibition at the Science Gallery in +Dublin, Ireland from April 17th to July 17th, 2009. During this event, +a radio-frequency identification (RFID) device was embedded into each +visitor's badge (as part of an interactive exhibit). RFID devices +exchange radio packets when they are at a close range from each other +(between 1~m and 1.5~m), in a peer-to-peer fashion. The data +collection process is described in detail +in~\cite{cattuto_dynamics_2010}. + +The devices are configured so that face-to-face interactions between +two individuals is accurately recorded with a probability of 99\% over +a period of 20~s, which is an appropriate time scale to record social +interaction. False positives are also extremely rare as RFID devices +have a very limited range and multiple radio packet exchanges are +required to record an interaction. + +The event in Dublin recorded more than 230,000 contact interactions +between more than 14,000 visitors. The data is made available both in +the form of daily-aggregated static +networks~\cite{noauthor_infectious_2011} and as a list of contact +interactions (each element being a timestamp and two nodes +IDs)~\cite{noauthor_infectious_2011-1}. + +\begin{figure}[ht] + \centering + \includegraphics[width=0.9\textwidth]{fig/sociopatterns.jpg} + \caption[Aggregated networks for two different days of the + SocioPatterns dataset.]{Aggregated networks for two different days + of the SocioPatterns dataset. Nodes are colored from red to purple + according to their arrival + time. (Source:~\cite{isella_whats_2011}.)}% + \label{fig:sp_plot} +\end{figure} + +The interactions times of the SocioPatterns dataset show that there +are limited interactions between visitors entering the exhibition more +than one hour apart (see~\autoref{fig:sp_plot}). A consequence of this +is that the network diameter of the daily aggregated graphs connects +visitors entering the venue at successive times, as can be seen in the +figure. + +Another interesting properties of these interactions is their +lengths. Most of the interaction last less than one minute, as can be +expected in the context of visitors in a museum exhibition. The +distribution of the interaction durations shows broad tails, decaying +slightly faster than a power law~\cite{isella_whats_2011}. + +The temporal network has also been used in a variety of contexts from +percolation analysis and dynamical spreading to community +detection~\cite{isella_whats_2011}. These studies have confirmed that +topological criteria detect efficiently the edges acting as bridges +between communities~\cite{girvan_community_2002, holme_attack_2002}. + +Many empirical temporal networks exhibit periodic +patterns~\cite{holme_modern_2015}. Many papers have explored +traditional network statistics and methods to uncover cyclic behaviour +in various datasets, mainly telecommunication +networks~\cite{jo_circadian_2012, aledavood_daily_2015, + holme_network_2003, aledavood_digital_2015}. + +Visualizations show significant variations in the patterns of the +daily aggregated graphs between weekdays and weekends (see the +SocioPatterns poster in appendix). This project will attempt to apply +the topological methods on an empirical dataset to try to detect +periodicity. + +\section{Computational environment}% +\label{sec:comp-envir} + +The analysis pipeline described in~\autoref{sec:analysis-pipeline} is +entirely implemented in Python. For these tests, we use Python~3.5, +with Numpy~1.15.1. The library +Dionysus~2.0.7~\cite{morozov_dionysus:_2018} is used for persistent +homology, zigzag persistence, and bottleneck distance. Networks are +handled by igraph~0.7.1, and machine-learning algorithms are provided +by Scikit-Learn~0.19.2~\cite{pedregosa_scikit-learn:_2011}. + +The program runs on a shared-memory system with 32 cores of 3.6~GHz, +756~GB of RAM, and 1.6~TB of storage. It runs Ubuntu Linux +16.04.5. Dionysus was compiled from the latest development version +using GCC 5.4.0 with the optimization level -O3. + +\section{Results}% +\label{sec:results} + +\subsection{Generative model}% +\label{sec:generative-model} + +For this study, random networks have been generated with the following +parameters (keeping the notations +from~\autoref{sec:gener-model-peri}): +\begin{itemize} +\item the base graph $G$ is an Erdős-Rényi graph with 40 nodes and an + edge probability of 90\%, +\item the total time range $T$ for the sequence of times is 200, +\item the frequency $f$ is 15/200. +\end{itemize} + +\autoref{fig:density} shows the density and sample times for a single +edge. A series of presence times like this is generated for each edge +in the base graph. + +\begin{figure}[ht] + \centering + \includegraphics[width=.85\linewidth]{fig/density.pdf} + \caption[Example of periodic density for edge times + generation.]{Example of periodic density for edge times generation + (blue), with random edge times (red), and the sliding windows + (grey).}% + \label{fig:density} +\end{figure} + +The generated temporal network is then subdivided into 20 +subnetworks. The sliding windows are also represented +on~\autoref{fig:density}. + +From these subnetworks, persistence is computed, in the form of +persistence diagrams. An example can be found +in~\autoref{fig:diagram}. + +\begin{figure}[ht] + \centering + \includegraphics[width=.5\linewidth]{fig/diagram.pdf} + \caption{Example persistence diagram.}% + \label{fig:diagram} +\end{figure} + +\autoref{fig:gen} represents the output of hierarchical clustering for +a random network, with zigzag and WRCF persistence, and the sliced +Wasserstein kernel and the bottleneck distance. This clustering is +representative of what is obtained by applying the same pipelines to +many temporal networks generated by the random model +of~\autoref{sec:gener-model-peri}. + +\begin{figure}[ht] + \centering + \begin{subfigure}[b]{0.46\linewidth} + \centering + \includegraphics[width=\linewidth]{fig/gen_zz_k.pdf} + \caption{Zigzag persistence, sliced Wasserstein kernel}% + \label{fig:gen_zz_k} + \end{subfigure}\qquad + \begin{subfigure}[b]{0.46\linewidth} + \centering + \includegraphics[width=\linewidth]{fig/gen_wrcf_k.pdf} + \caption{WRCF, sliced Wasserstein kernel}% + \label{fig:gen_wrcf_k} + \end{subfigure} + + \begin{subfigure}[b]{0.46\linewidth} + \centering + \includegraphics[width=\linewidth]{fig/gen_zz_b.pdf} + \caption{Zigzag persistence, bottleneck distance}% + \label{fig:gen_zz_b} + \end{subfigure}\qquad + \begin{subfigure}[b]{0.46\linewidth} + \centering + \includegraphics[width=\linewidth]{fig/gen_wrcf_b.pdf} + \caption{WRCF, bottleneck distance}% + \label{fig:gen_wrcf_b} + \end{subfigure} + \caption{Hierarchical clustering with 10 clusters of a random + temporal network.}% + \label{fig:gen} +\end{figure} + +As we can see on the figure, the hierarchical clustering algorithm is +able to determine the periodicity of the temporal networks when using +the sliced Wasserstein kernel. However, with the simple bottleneck +distance, the periodicity is not correctly detected. The periodicity +detection can be confirmed by moving further up in the dendrogram of +the clustering algorithm. With only 2 or 3 clusters, the low and high +sections of the density (\autoref{fig:density}) are still accurately +classified with the sliced Wasserstein kernel, while the subnetworks +are distributed randomly among the clusters with the bottleneck +distance. + +Somewhat less clear is the comparison between zigzag persistence and +WRCF persistence. When generating many samples from the random +temporal network model, WRCF and sliced Wasserstein kernel clustering +is noisier and less consistent in its periodicity detection than its +zigzag persistence counterpart. This indicates that the aggregation of +the temporal subnetworks lead to the creation of artificial +topological features that introduce noise in the +dataset. (See~\autoref{sec:zigzag-persistence-1} for details on why +aggregation introduce artificial simplices in the temporal network.) + +\subsection{SocioPatterns dataset}% +\label{sec:soci-datas} + +\begin{figure}[ht] + \centering + \begin{subfigure}[b]{0.8\linewidth} + \centering + \includegraphics[width=.85\linewidth]{fig/sp_zz_k.pdf} + \caption{Zigzag persistence, sliced Wasserstein kernel}% + \label{fig:sp_zz_k} +\end{subfigure} +\begin{subfigure}[b]{0.8\linewidth} + \centering + \includegraphics[width=.85\linewidth]{fig/sp_wrcf_k.pdf} + \caption{WRCF, sliced Wasserstein kernel}% + \label{fig:sp_wrcf_k} +\end{subfigure} +\caption{Hierarchical clustering with 10 clusters of the SocioPatterns + dataset.}% +\label{fig:sp} +\end{figure} + +In the study of the SocioPatterns dataset, we expect to uncover a +periodicity on a scale of a day. Therefore, we would like to partition +the time range of the dataset into windows approximately the length of +a day. However, this leads to very sparse subnetworks, which do not +exhibit enough topological features for a complete study. Since the +main periodicity in the dataset is expected to be a weekday/weekend +succession, we choose a resolution of two days. + +The previous section has demonstrated that the sliced Wasserstein +kernel was the most suitable to uncover periodicity in a temporal +network. The results, with zigzag persistence and WRCF, of the +hierarchical clustering algorithm are shown in~\autoref{fig:sp}. + +\begin{figure}[ht] + \centering + \begin{subfigure}[b]{0.45\linewidth} + \includegraphics[width=\linewidth]{fig/gen_zz_gram1.pdf} + \caption{Generative model}% + \label{fig:gram_gen} + \end{subfigure}\qquad + \begin{subfigure}[b]{0.45\linewidth} + \includegraphics[width=\linewidth]{fig/sp_zz_gram1.pdf} + \caption{SocioPatterns dataset}% + \label{fig:gram_sp} + \end{subfigure} + \caption{Gram matrices of the sliced Wasserstein kernel with zigzag + persistence.}% + \label{fig:gram} +\end{figure} + +However, the subnetworks do not cluster periodically in either +case. This is confirmed by visualizing the Gram matrix of the sliced +Wasserstein kernel (\autoref{fig:gram}). The Gram matrix obtained with +the generative model exhibit a cyclical pattern, while the one from +the SocioPatterns dataset do not show enough distinctions between the +subnetworks. + +It is unclear whether this is due to the analysis pipeline, or to the +temporal network itself not exhibiting enough periodicity on a +topological level. To confirm this, one would need to compare our +analysis with one using traditional network statistics, such as the +ones in~\cite{jo_circadian_2012, aledavood_daily_2015, + holme_network_2003, aledavood_digital_2015}. Other empirical +networks, such as telecommunication networks, may also exhibit more +obvious cyclical patterns, where topological features might be useful. + \chapter{Conclusions}% \label{cha:conclusions} +\section{Topological data analysis of temporal networks}% +\label{sec:topol-data-analys} + +Periodicity detection on our generative model has proven +successful. More importantly, topological features and persistent +homology seem to play an important part in the classification +task. The general idea of partitioning the time range of a temporal +network into sliding windows, and running an unsupervised clustering +algorithm, works in the context of periodicity detection. + +More generally, we have introduced persistent homology and topological +data analysis methods for the study of temporal networks. Building on +previous work clustering different temporal network generative models +with persistent homology~\cite{price-wright_topological_2015}, we have +expanded both the methods used and the applications, solving the +real-world problem of periodicity detection. All in all, it is clear +that persistent homology is a promising new direction for the study of +temporal networks. + +Topological data analysis is a recent field, with new methods and +approaches being constantly developed and improved. In this project, +we have compared different approaches. In the context of periodicity +detection, zigzag persistence is a small improvement over the +topological analysis of aggregated graphs using weight rank clique +filtration. If this result was confirmed by other studies, it would be +an interesting development, as it would imply that the temporal aspect +is essential and cannot be discarded easily when studying temporal +networks. + +One of the most active research areas of topological data analysis has +been its applications in machine learning. Considerable efforts have +been deployed in the development of various vectorization techniques +to embed topological information into a feature space suitable for +statistical learning algorithms. In this project, a few of these +methods have been compared for their theoretical guarantees, and their +practical applications in periodicity detection. From a mathematical +point of view, kernels seem the most promising approach, by offering +strong stability properties and algebraic structures on the feature +space. This development leads to a broader class of applications in +machine learning where topological analysis can be useful. These +theoretical advances have translated into much better results for +periodicity detection. The simple bottleneck distance in the space of +diagram (with a structure of metric space) was not able to determine +any kind of periodicity in the random networks, whereas the sliced +Wasserstein kernel (embedding persistence diagrams in its RKHS, with +an metric equivalent to the distance in the space of persistence +diagrams) picked up the period accurately. This confirms previous work +on shape classification, where kernels on persistence diagrams +significantly outperformed other feature +embeddings~\cite{kusano_kernel_2017, reininghaus_stable_2015, + carriere_sliced_2017}. + +Finally, we have tried to apply the same analysis to detect +periodicity on real-world data, the SocioPatterns dataset. Our model +was not able to detect a periodicity with a change of patterns between +the weekdays and the weekends. This is unclear whether it is due to +the limits in some part of our analysis pipeline, or to the +periodicity in the network being non-topological in nature. A future +study might focus on combining topological features with traditional +network statistics. + +\section{Future work}% +\label{sec:future-work} + +Further study of topological features of temporal networks is +needed. We could imagine other applications than periodicity +detection, such as community +detection~\cite{girvan_community_2002}. Many standard methods are +difficult to adapt to temporal network models, and computational +topology could bring an additional perspective in these tasks, by +complementing traditional network statistics. + +In the specific context of periodicity detection, this analysis can be +expanded by varying the parameters such as the resolution and the +overlap. It could be especially useful for inferring the period in a +temporal network. + +One should also explore the other vectorization methods in the context +of periodicity detection. It would be interesting to know how +persistence images, or the other kernels, perform in this task. Last +but not least, it is essential to compare the performance of the +topological features with more traditional network statistics. It +would also be interesting to combine both aspects and use both set of +features in machine-learning tasks. + +Finally, temporal networks seem to be the ideal context to apply +multidimensional persistence. For instance, the weight rank clique +filtration adds a ``weight'' dimension to the existing time +dimension. In theory, it would be possible to use this by constructing +a 2-parameter filtration on the network, and computing persistence on +it. + +\appendix + +\chapter{Topology}% +\label{cha:topology} + +In the following chapter, we recall a few essential definitions in +topology. This is in large part taken from~\cite{golse_mat321_2015}. + +In this chapter, all vector spaces will be over a field $\mathbb{K}$, +which is either the field of real numbers $\mathbb{R}$ or the field of +complex numbers $\mathbb{C}$. + +\section{Metric spaces}% +\label{sec:metric-spaces} + +\begin{defn}[Distance, metric space] + An application $d : X \times X \mapsto \mathbb{R}^+$ is a \emph{distance} + over $X$ if + \begin{enumerate}[(i)] + \item $\forall x,y\in X,\; d(x,y) = 0 \Leftrightarrow x=y$ (separation), + \item $\forall x,y\in X,\; d(x,y) = d(y,x)$ (symmetry), + \item $\forall x,y,z\in X,\; d(x,y) + d(y,z) \geq d(x,z)$ (triangle inequality). + \end{enumerate} + + In this case, $(X, d)$ is called a \emph{metric space}. +\end{defn} + +If $Y$ is a subset of $X$ and $X$ is a metric space (with the distance +$d$), then $(Y, d)$ is immediately a metric space itself. $d_Y$ is +called the \emph{induced metric} on $Y$. + +%% example? + +If $(X,d)$ is metric space, then for all $x\in X$ and $r>0$, the set +\[ B(x,r) := \{y\in X : d(x,y) < r\} \] is called the \emph{open ball} +centered at $x$ and of radius $r$. The \emph{closed ball} centered at +$x$ and of radius $r$ is defined by +\[ B_c(x,r) := \{y\in X : d(x,y) \leq r\}. \] + +%% example/figure? + +An important class of metric spaces is the one where the set $X$ is +itself a normed vector space. + +\begin{defn}[Norm] + Let $V$ be a vector space over $\mathbb{K}$. An application + $N: V\mapsto\mathbb{R}^+$ is a \emph{norm} over $V$ if + \begin{enumerate}[(i)] + \item $\forall x\in V,\; N(x) = 0 \Leftrightarrow x=0$, + \item + $\forall x\in V, \forall \lambda\in\mathbb{K},\; N(\lambda x) = + \lvert\lambda\rvert N(x)$, + \item $\forall x,y\in V,\; N(x) + N(y) \geq N(x+Y)$. + \end{enumerate} +\end{defn} + +Let $(V,N)$ be a normed vector space. For every subset $X$ of $V$, one +can define $d(x,y) := N(x-y)$ for all $x,y\in X$. Using the properties +of the norm $N$, one can check easily that $d$ is a distance, and +therefore $(X,d)$ is a metric space. + +%% examples? + +There are many norms possible on a vector space. This brings the need +to compare these various norms. + +\begin{defn}[Norm equivalence] + Let $V$ be a vector space. Two norms $N_1$ and $N_2$ on $V$ are said + to be \emph{equivalent} if there are two constants $C_1$ and $C_2$ + such that + \[ \forall x\in V,\quad N_1(x) \leq C_1 N_2(x) \quad\text{and}\quad N_2(x) + \leq C_2 N_1(x). \] +\end{defn} + +Geometrically speaking, two norms are equivalent if the unit ball for +the norm $N_1$ contains a non-empty ball centred at 0 for the norm +$N_2$, and vice-versa. + +%% all norms are equivalent in a vector space of finite dimension? + +\section{Completeness}% +\label{sec:completeness} + +\begin{defn}[Convergence] + A sequence ${(x_n)}_{n\in\mathbb{N}}$ of elements of a metric space + $(X,d)$ \emph{converges} to a limit $x$ if + \[ \lim_{n\rightarrow\infty} d(x_n,x) = 0. \] +\end{defn} + +\begin{defn}[Cauchy sequence] + A sequence ${(x_n)}_{n\in\mathbb{N}}$ of elements of a metric space + $(X,d)$ is a \emph{Cauchy sequence} if + \[ \forall\varepsilon>0, \exists n_0\in\mathbb{N},\; \text{such + that:}\; \forall n,m\geq n_0,\; d(x_n, x_m) < \varepsilon. \] +\end{defn} + +Note that every convergent sequence is a Cauchy sequence, but the +opposite is not true in general. %% counter-example? + +\begin{defn}[Completeness] + A metric space $(X,d)$ is \emph{complete} if, and only if, every + Cauchy sequence converges to an element of $X$. +\end{defn} + +%% examples? + +%% properties? would need open/close + +\begin{defn}[Banach space] + A \emph{Banach space} is a complete normed vector space. +\end{defn} + +\section{Hilbert spaces}% +\label{sec:hilbert-spaces} + +In this section, vector spaces are defined over~$\mathbb{C}$. The +theory extends easily to vector spaces over~$\mathbb{R}$. + +An application $L$ between two $\mathbb{C}$-vector spaces $V$ and $W$ +is said to be \emph{anti-linear} if +\[ \forall \lambda,\mu\in\mathbb{C}, \forall x,y\in V,\; L(\lambda x + + \mu y) = \bar{\lambda} L(x) + \bar{\mu} y. \] + +\begin{defn}[Hermitian product] + An application $\langle\cdot,\cdot\rangle : V \times V \mapsto \mathbb{C}$ is + \begin{enumerate}[(i)] + \item a \emph{sesquilinear form} if $x\mapsto\langle x,y \rangle$ is + linear and $y\mapsto\langle x,y \rangle$ is anti-linear, + \item a \emph{Hermitian form} if it is sesquilinear and + $\langle x,y \rangle = \overline{\langle y,x \rangle}$ for all + $x,y\in V$, + \item a \emph{Hermitian product} if it is a Hermitian form positive + definite, i.e.\ if $\langle x,x \rangle > 0$ for all $x \neq 0$. + \end{enumerate} +\end{defn} + +\begin{rem} + In the case of vector spaces over $\mathbb{R}$, sesquilinear forms + are simply bilinear, Hermitian forms are symmetric bilinear, and + Hermitian products are inner products. +\end{rem} + +\begin{prop}[Cauchy-Schwartz inequality] + Let $\langle\cdot,\cdot\rangle$ be a Hermitian product over + $V$. Then, for all $x,y\in V$, + \[ \lvert\langle x,y \rangle\rvert \leq \sqrt{\langle x,x \rangle} + \sqrt{\langle y,y \rangle}, \] where the two sides are equal if + and only if $x$ and $y$ are linearly dependent. +\end{prop} + +\begin{proof} + Suppose that $x \neq 0$ and $y \neq 0$ (otherwise the proposition is + obvious). For all $t > 0$, we compute + \[ \langle x - ty, x - ty \rangle = \langle x,x \rangle - 2t + \mathrm{Re}\langle x,y \rangle + t^2 \langle y,y \rangle \geq + 0. \] + + Thus, for all $t > 0$, + \[ 2 \mathrm{Re} \langle x,y \rangle \leq \frac{1}{t}\langle x,x + \rangle + t \langle y,y \rangle. \] + + We minimize the right-hand side by choosing + \[ t = \sqrt{\frac{\langle x,x \rangle}{\langle y,y \rangle}}, \] + thus + \[ \mathrm{Re} \langle x,y \rangle \leq \sqrt{\langle x,x \rangle} + \sqrt{\langle y,y \rangle}. \] + + The inequality follows by replacing $x$ by $e^{i\theta}x$ and using the fact that + \[ \forall z\in\mathbb{C},\; \lvert z \rvert = + \sup_{\theta\in\mathbb{R}} \mathrm{Re}\left(e^{i\theta} z + \right). \] + + The equality case follows from setting + $\langle x - ty, x - ty \rangle = 0$. +\end{proof} + +If $\langle\cdot,\cdot\rangle$ is a Hermitian product over $V$, it can +be verified easily that the form +\[ \lVert\cdot\rVert : x \mapsto \sqrt{\langle x,x \rangle} \] is a +norm over $V$. The triangle inequality comes from the Cauchy-Schwartz +formula. %% proof? + +\begin{defn}[Pre-Hilbert space] + A \emph{pre-Hilbert space} is a vector space $V$ with a Hermitian + product $\langle\cdot,\cdot\rangle$ and the associated norm + $\lVert\cdot\rVert$. It is a metric space for the distance + $d(x,y) := \lVert x-y \rVert$. +\end{defn} + +\begin{defn}[Hilbert space] + A pre-Hilbert space $H$ is a \emph{Hilbert space} if + $(H, \lVert\cdot\rVert)$ is a Banach space. +\end{defn} + +\addtocounter{chapter}{1} +\addcontentsline{toc}{chapter}{\protect\numberline{\thechapter} Infectious SocioPatterns poster} +\includepdf{fig/infectious_poster.pdf} +% \includepdf{fig/infectious_poster_highres.pdf}\label{cha:infectious_poster} + + + +\chapter{Code}% +\label{cha:code} + +\section{\texttt{zigzag.py}}% +\label{sec:zigzagpy} + +\inputminted{python}{zigzag.py} + +\section{\texttt{wrcf.py}}% +\label{sec:wrcfpy} + +\inputminted{python}{wrcf.py} + +\section{\texttt{sliced\_wasserstein.py}}% +\label{sec:sliced_wassersteinpy} + +\inputminted{python}{sliced_wasserstein.py} + +\section{\texttt{generative.py}}% +\label{sec:generativepy} + +\inputminted{python}{generative.py} + +\section{\texttt{sociopatterns.py}}% +\label{sec:sociopatternspy} + +\inputminted{python}{sociopatterns.py} + +\section{\texttt{clustering.py}}% +\label{sec:clusteringpy} + +\inputminted{python}{clustering.py} + \backmatter% % \nocite{*} diff --git a/dissertation/fig/density.pdf b/dissertation/fig/density.pdf new file mode 100644 index 0000000..f25f129 Binary files /dev/null and b/dissertation/fig/density.pdf differ diff --git a/dissertation/fig/diagram.pdf b/dissertation/fig/diagram.pdf new file mode 100644 index 0000000..126fc68 Binary files /dev/null and b/dissertation/fig/diagram.pdf differ diff --git a/dissertation/fig/gen_wrcf_b.pdf b/dissertation/fig/gen_wrcf_b.pdf new file mode 100644 index 0000000..9b26424 Binary files /dev/null and b/dissertation/fig/gen_wrcf_b.pdf differ diff --git a/dissertation/fig/gen_wrcf_k.pdf b/dissertation/fig/gen_wrcf_k.pdf new file mode 100644 index 0000000..a4ef70c Binary files /dev/null and b/dissertation/fig/gen_wrcf_k.pdf differ diff --git a/dissertation/fig/gen_zz_b.pdf b/dissertation/fig/gen_zz_b.pdf new file 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a/dissertation/fig/sociopatterns.jpg b/dissertation/fig/sociopatterns.jpg new file mode 100644 index 0000000..ea00339 Binary files /dev/null and b/dissertation/fig/sociopatterns.jpg differ diff --git a/dissertation/fig/sociopatterns.pdf b/dissertation/fig/sociopatterns.pdf new file mode 100644 index 0000000..0c88dfc Binary files /dev/null and b/dissertation/fig/sociopatterns.pdf differ diff --git a/dissertation/fig/sociopatterns.png b/dissertation/fig/sociopatterns.png new file mode 100644 index 0000000..40770bf Binary files /dev/null and b/dissertation/fig/sociopatterns.png differ diff --git a/dissertation/fig/sp_wrcf_b.pdf b/dissertation/fig/sp_wrcf_b.pdf new file mode 100644 index 0000000..4420552 Binary files /dev/null and b/dissertation/fig/sp_wrcf_b.pdf differ diff --git a/dissertation/fig/sp_wrcf_k.pdf b/dissertation/fig/sp_wrcf_k.pdf new file mode 100644 index 0000000..067007f Binary files /dev/null and b/dissertation/fig/sp_wrcf_k.pdf differ diff --git a/dissertation/fig/sp_zz_b.pdf b/dissertation/fig/sp_zz_b.pdf new file mode 100644 index 0000000..8598f94 Binary files /dev/null and b/dissertation/fig/sp_zz_b.pdf differ diff --git a/dissertation/fig/sp_zz_gram1.pdf b/dissertation/fig/sp_zz_gram1.pdf new file mode 100644 index 0000000..7e13ffe Binary files /dev/null and b/dissertation/fig/sp_zz_gram1.pdf differ diff --git a/dissertation/fig/sp_zz_k.pdf b/dissertation/fig/sp_zz_k.pdf new file mode 100644 index 0000000..2b2fc90 Binary files /dev/null and b/dissertation/fig/sp_zz_k.pdf differ diff --git a/dissertation/generative.py b/dissertation/generative.py new file mode 100644 index 0000000..a916f68 --- /dev/null +++ b/dissertation/generative.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 + +import numpy as np +import igraph as ig +import dionysus as d + +import multiprocessing +# from dask.distributed import Client + +from zigzag import sliding_windows, zigzag_network +from wrcf import wrcf_diagram +from sliced_wasserstein import diagram_array, SW_approx + +import dill + + +def random_edge_presences(T, f): + """Generate random times sampled over a periodic distribution. + + :param T: time range + :param f: frequency + + :return: an array of times. + """ + density = np.sin(f * np.arange(T)) + 1 + density /= np.sum(density) + samplesize = np.random.randint(T//2) + times = np.random.choice(np.arange(T), size=samplesize, replace=False, p=density) + times = np.sort(times) + return times + + +def remove_inf(dgm): + """Remove infinite points in a persistence diagram. + + :param dgm: Diagram + + :return: the same diagram without the infinite points. + """ + res = d.Diagram() + for p in dgm: + if p.death != np.inf: + res.append(p) + return res + + +## Global parameters +NODES = 40 +EDGE_PROB = 0.9 +TIME_RANGE = 200 +FREQ = 15/TIME_RANGE +N_WINDOWS = 20 + +## Computations +ZIGZAG_PERS = True +WRCF_PERS = True +SW_KERNEL = True +BOTTLENECK_DIST = True + + +if __name__=="__main__": + print("Generating random temporal network...", end="", flush=True) + basegraph = ig.Graph.Erdos_Renyi(NODES, EDGE_PROB) + g = ig.Graph() + g.add_vertices(len(basegraph.vs)) + for e in basegraph.es: + times = random_edge_presences(TIME_RANGE, FREQ) + for t in times: + g.add_edge(e.source, e.target, time=t) + print("done.") + + print("Temporal partitioning...", end="", flush=True) + wins = sliding_windows(g, 1/N_WINDOWS) + print("done.") + + pool = multiprocessing.Pool(processes=multiprocessing.cpu_count()) + + if ZIGZAG_PERS: + print("Zigzag persistence...", end="", flush=True) + zz_dgms = pool.map(zigzag_network, wins) + dill.dump(zz_dgms, open("generative/zz_dgms.dill", "wb")) + print("done, saved.") + + if WRCF_PERS: + print("WRCF...", end="", flush=True) + ## Collapse each subnetwork into a static graph: the weight is the + ## number of appearances of each edge + for w in wins: + w.es["time"] = np.repeat(1, len(w.es["time"])) + w.simplify(combine_edges="sum") + w.es["weight"] = w.es["time"] + del w.es["time"] + wrcf_dgms = pool.map(wrcf_diagram, wins) + dill.dump(wrcf_dgms, open("generative/wrcf_dgms.dill", "wb")) + print("done.") + + pool.terminate() + + if ZIGZAG_PERS and SW_KERNEL: + print("Sliced Wasserstein Kernel (zigzag)...", end="", flush=True) + zz_dgms1 = [dgm[1] for dgm in zz_dgms if len(dgm) > 1] + zz_gram1 = np.array([[SW_approx(zz_dgms1[i], zz_dgms1[j], 10) + for i in range(len(zz_dgms1))] for j in range(len(zz_dgms1))]) + dill.dump(zz_gram1, open("generative/zz_gram1.dill", "wb")) + print("done, saved.") + if WRCF_PERS and SW_KERNEL: + print("Sliced Wasserstein Kernel (WRCF)...", end="", flush=True) + wrcf_dgms1 = [dgm[1] for dgm in wrcf_dgms if len(dgm) > 1] + wrcf_gram1 = np.array([[SW_approx(wrcf_dgms1[i], wrcf_dgms1[j], 10) + for i in range(len(wrcf_dgms1))] for j in range(len(wrcf_dgms1))]) + dill.dump(wrcf_gram1, open("generative/wrcf_gram1.dill", "wb")) + print("done, saved.") + + if ZIGZAG_PERS and BOTTLENECK_DIST: + print("Bottleneck distance (zigzag)...", end="", flush=True) + zz_dgms1 = list(map(remove_inf, zz_dgms1)) + zz_distmat = np.array([[d.bottleneck_distance(zz_dgms1[i], zz_dgms1[j]) + for i in range(len(zz_dgms1))] for j in range(len(zz_dgms1))]) + dill.dump(zz_distmat, open("generative/zz_distmat.dill", "wb")) + print("done, saved.") + if WRCF_PERS and BOTTLENECK_DIST: + print("Bottleneck distance (WRCF)...", end="", flush=True) + wrcf_dgms1 = list(map(remove_inf, wrcf_dgms1)) + wrcf_distmat = np.array([[d.bottleneck_distance(wrcf_dgms1[i], wrcf_dgms1[j]) + for i in range(len(wrcf_dgms1))] for j in range(len(wrcf_dgms1))]) + dill.dump(wrcf_distmat, open("generative/wrcf_distmat.dill", "wb")) + print("done, saved.") + diff --git a/dissertation/preamble.tex b/dissertation/preamble.tex index ec99645..27b47cd 100644 --- a/dissertation/preamble.tex +++ b/dissertation/preamble.tex @@ -1,8 +1,21 @@ \usepackage{fontspec} -\setmainfont{Linux Libertine O} -\setsansfont{Linux Biolinum O} -\setmonofont[Scale=0.83]{Inconsolata} +% \setmainfont{Linux Libertine O} +% \setsansfont{Linux Biolinum O} +% \setmonofont[Scale=0.83]{Inconsolata} + +\setmainfont{Libertinus Serif} +\setsansfont{Libertinus Sans} +\setmonofont[Scale=0.95]{Inconsolata} + +\usepackage{amsmath} +\usepackage{amssymb} +\usepackage{amsthm} +\usepackage{unicode-math} +%\setmathfont{Libertinus Math} +\usepackage[ruled]{algorithm2e} + +\newcommand*\diff{\mathop{}\!\mathrm{d}} \usepackage{polyglossia} \setdefaultlanguage[variant=british]{english} @@ -10,26 +23,26 @@ \usepackage{graphicx} \usepackage[dvipsnames]{xcolor} \usepackage{wrapfig} +\usepackage{caption} \usepackage{subcaption} \usepackage{lettrine} -\usepackage{amssymb, amsmath} -\usepackage{amsthm} +\usepackage{thmtools} \theoremstyle{plain} \newtheorem{thm}{Theorem}[chapter] -\newcommand{\thmautorefname}{theorem} +%\newcommand{\thmautorefname}{theorem} \newtheorem{lem}[thm]{Lemma} -\newcommand{\lemautorefname}{lemma} +%\newcommand{\lemautorefname}{lemma} \newtheorem{cor}[thm]{Corollary} -\newcommand{\corautorefname}{corollary} +%\newcommand{\corautorefname}{corollary} \newtheorem{prop}[thm]{Proposition} -\newcommand{\propautorefname}{proposition} +%\newcommand{\propautorefname}{proposition} \theoremstyle{definition} \newtheorem{defn}{Definition}[chapter] -\newcommand{\defnautorefname}{definition} +%\newcommand{\defnautorefname}{definition} \newtheorem{expl}{Example}[chapter] -\newcommand{\explautorefname}{example} +%\newcommand{\explautorefname}{example} \theoremstyle{remark} \newtheorem*{rem}{Remark} \newtheorem*{note}{Note} @@ -39,10 +52,11 @@ \usepackage{tikz-network} \usepackage{tikz} -\usetikzlibrary{patterns,backgrounds,positioning,chains,lindenmayersystems} +\usetikzlibrary{patterns,backgrounds,positioning,chains,lindenmayersystems,intersections} -\usepackage[style=numeric-comp,backref,url=false]{biblatex} -\bibliography{TDA,temporalgraphs} +\usepackage{csquotes} +\usepackage[backend=biber,style=numeric-comp,backref,url=false,minnames=3]{biblatex} +\bibliography{TDA,temporalgraphs,Other} \usepackage{pdfpages} @@ -61,9 +75,12 @@ linkcolor=MidnightBlue, filecolor=MidnightBlue, urlcolor=MidnightBlue, - citecolor=Green + citecolor=MidnightBlue } +\usepackage{minted} +\setminted{autogobble,fontsize=\footnotesize,linenos,stepnumber=5} + %% Pour la classe memoir /!\ %% Marges @@ -72,12 +89,12 @@ \checkandfixthelayout% %% Numérotation des divisions logiques -\setsecnumdepth{subsection} -\maxsecnumdepth{subsection} +\setsecnumdepth{subsubsection} +\maxsecnumdepth{subsubsection} %% Profondeur de la ToC -\settocdepth{subsection} -\maxtocdepth{subsection} +\settocdepth{subsubsection} +\maxtocdepth{subsubsection} %% Style des titres des divisions logiques \setsecheadstyle{\Large\scshape} @@ -95,6 +112,7 @@ %% Couleurs %\definecolor{purpletouch}{RGB}{103,30,117} \definecolor{bleux}{RGB}{0,62,92} +\definecolor{OxfordBlue}{RGB}{0,33,71} \author{Dimitri Lozeve} \date{September 2018} diff --git a/dissertation/sliced_wasserstein.py b/dissertation/sliced_wasserstein.py new file mode 100644 index 0000000..6e8c92d --- /dev/null +++ b/dissertation/sliced_wasserstein.py @@ -0,0 +1,54 @@ +import numpy as np +import dionysus as d + + +def diagram_array(dgm): + """Convert a Dionysus diagram to a Numpy array. + + :param dgm: Dionysus Diagram + + :return: a Numpy array of tuples representing the points in the + diagram. + """ + res = [] + for p in dgm: + if p.death != np.inf: + res.append([p.birth, p.death]) + return np.array(res) + + +def SW_approx(dgm1, dgm2, M): + """Approximate computation of the Sliced Wasserstein kernel. + + :param dgm1: first Diagram + :param dgm2: second Diagram + :param M int: number of directions + + :return: The approximate value of the Sliced Wasserstein kernel of + dgm1 and dgm2, sampled over M dimensions. + """ + dgm1 = diagram_array(dgm1) + dgm2 = diagram_array(dgm2) + if dgm1.size == 0 or dgm2.size == 0: + return 0 + # Add \pi_\delta(dgm1) to dgm2 and vice-versa + proj1 = dgm1.dot([1, 1])/np.sqrt(2) + proj2 = dgm2.dot([1, 1])/np.sqrt(2) + dgm1 = np.vstack((dgm1, np.vstack((proj2, proj2)).T)) + dgm2 = np.vstack((dgm2, np.vstack((proj1, proj1)).T)) + SW = 0 + theta = -np.pi/2 + s = np.pi/M + for i in range(M): + # Project each diagram on the direction theta + vec = [1, np.arctan(theta)] + vec = vec / np.linalg.norm(vec) + V1 = dgm1.dot(vec) + V2 = dgm2.dot(vec) + # Sort the projections + V1.sort() + V2.sort() + # l1-distance between the projections + SW = SW + s * np.sum(np.abs(V1 - V2)) + theta = theta + s + return 1/np.pi * SW diff --git a/dissertation/sociopatterns.py b/dissertation/sociopatterns.py new file mode 100644 index 0000000..6c9c53d --- /dev/null +++ b/dissertation/sociopatterns.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 + +import numpy as np +import igraph as ig +import dionysus as d + +import multiprocessing +# from dask.distributed import Client + +from zigzag import sliding_windows, zigzag_network +from wrcf import wrcf_diagram +from sliced_wasserstein import diagram_array, SW_approx + +import dill + + +def remove_inf(dgm): + """Remove infinite points in a persistence diagram. + + :param dgm: Diagram + + :return: the same diagram without the infinite points. + """ + res = d.Diagram() + for p in dgm: + if p.death != np.inf: + res.append(p) + return res + + +## Global parameters +N_WINDOWS = 40 + +## Computations +ZIGZAG_PERS = True +WRCF_PERS = True +SW_KERNEL = True +BOTTLENECK_DIST = True + + +if __name__=="__main__": + print("Loading SocioPatterns dataset...", end="", flush=True) + g = ig.read("data/sociopatterns/infectious/infectious.graphml") + del g.es["id"] + # print(g.summary()) + print("done.") + + print("Temporal partitioning...", end="", flush=True) + wins = sliding_windows(g, 1/N_WINDOWS) + print("done.") + + pool = multiprocessing.Pool(processes=multiprocessing.cpu_count()) + + if ZIGZAG_PERS: + print("Zigzag persistence...", end="", flush=True) + zz_dgms = pool.map(zigzag_network, wins) + dill.dump(zz_dgms, open("sociopatterns/zz_dgms.dill", "wb")) + print("done, saved.") + + if WRCF_PERS: + print("WRCF...", end="", flush=True) + ## Collapse each subnetwork into a static graph: the weight is the + ## number of appearances of each edge + for w in wins: + w.es["time"] = np.repeat(1, len(w.es["time"])) + w.simplify(combine_edges="sum") + w.es["weight"] = w.es["time"] + del w.es["time"] + wrcf_dgms = pool.map(wrcf_diagram, wins) + dill.dump(wrcf_dgms, open("sociopatterns/wrcf_dgms.dill", "wb")) + print("done.") + + pool.terminate() + + if ZIGZAG_PERS and SW_KERNEL: + print("Sliced Wasserstein Kernel (zigzag)...", end="", flush=True) + zz_dgms1 = [dgm[1] for dgm in zz_dgms if len(dgm) > 1] + zz_gram1 = np.array([[SW_approx(zz_dgms1[i], zz_dgms1[j], 10) + for i in range(len(zz_dgms1))] for j in range(len(zz_dgms1))]) + dill.dump(zz_gram1, open("sociopatterns/zz_gram1.dill", "wb")) + print("done, saved.") + if WRCF_PERS and SW_KERNEL: + print("Sliced Wasserstein Kernel (WRCF)...", end="", flush=True) + wrcf_dgms1 = [dgm[1] for dgm in wrcf_dgms if len(dgm) > 1] + wrcf_gram1 = np.array([[SW_approx(wrcf_dgms1[i], wrcf_dgms1[j], 10) + for i in range(len(wrcf_dgms1))] for j in range(len(wrcf_dgms1))]) + dill.dump(wrcf_gram1, open("sociopatterns/wrcf_gram1.dill", "wb")) + print("done, saved.") + + if ZIGZAG_PERS and BOTTLENECK_DIST: + print("Bottleneck distance (zigzag)...", end="", flush=True) + zz_dgms1 = list(map(remove_inf, zz_dgms1)) + zz_distmat = np.array([[d.bottleneck_distance(zz_dgms1[i], zz_dgms1[j]) + for i in range(len(zz_dgms1))] for j in range(len(zz_dgms1))]) + dill.dump(zz_distmat, open("sociopatterns/zz_distmat.dill", "wb")) + print("done, saved.") + if WRCF_PERS and BOTTLENECK_DIST: + print("Bottleneck distance (WRCF)...", end="", flush=True) + wrcf_dgms1 = list(map(remove_inf, wrcf_dgms1)) + wrcf_distmat = np.array([[d.bottleneck_distance(wrcf_dgms1[i], wrcf_dgms1[j]) + for i in range(len(wrcf_dgms1))] for j in range(len(wrcf_dgms1))]) + dill.dump(wrcf_distmat, open("sociopatterns/wrcf_distmat.dill", "wb")) + print("done, saved.") + + + diff --git a/dissertation/temporalgraphs.bib b/dissertation/temporalgraphs.bib index 32c258c..60d8144 100644 --- a/dissertation/temporalgraphs.bib +++ b/dissertation/temporalgraphs.bib @@ -1,4 +1,36 @@ +@article{girvan_community_2002, + title = {Community structure in social and biological networks}, + volume = {99}, + issn = {0027-8424}, + url = {http://arxiv.org/abs/cond-mat/0112110}, + doi = {10.1073/pnas.}, + abstract = {A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known-a collaboration network and a food web-and find that it detects significant and informative community divisions in both cases.}, + pages = {7821--7826}, + number = {12}, + journaltitle = {Proceedings of the National Academy of Sciences of United States of America}, + author = {Girvan, M. and Newman, M. E. J}, + date = {2002}, + pmid = {12060727}, + keywords = {Models, Neural Networks (Computer), Theoretical, Algorithms, Animals, Community Networks, Computer Simulation, Humans, Nerve Net, Nerve Net: physiology, Social Behavior}, + file = {Attachment:/home/dimitri/Zotero/storage/X3C73Q5I/Girvan, Newman - 2002 - Community structure in social and biological networks.pdf:application/pdf} +} + +@book{newman_networks:_2010, + location = {Oxford ; New York}, + title = {Networks: an introduction}, + isbn = {978-0-19-920665-0}, + shorttitle = {Networks}, + abstract = {"The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks. The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas. Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks"--}, + pagetotal = {772}, + publisher = {Oxford University Press}, + author = {Newman, M. E. J.}, + date = {2010}, + note = {{OCLC}: ocn456837194}, + keywords = {Engineering systems, Network analysis (Planning), Social systems, System analysis, Systems biology}, + file = {Mark_Newman_Networks_An_Introduction.pdf:/home/dimitri/Zotero/storage/FDMM48IV/Mark_Newman_Networks_An_Introduction.pdf:application/pdf} +} + @article{tabourier_predicting_2016, title = {Predicting links in ego-networks using temporal information}, volume = {5}, @@ -425,21 +457,206 @@ file = {Snapshot:/home/dimitri/Zotero/storage/CYSLT5MA/10.html:text/html} } -@article{tomita_worst-case_2006, - title = {The worst-case time complexity for generating all maximal cliques and computational experiments}, - volume = {363}, - issn = {0304-3975}, - url = {http://www.sciencedirect.com/science/article/pii/S0304397506003586}, - doi = {10.1016/j.tcs.2006.06.015}, - series = {Computing and Combinatorics}, - abstract = {We present a depth-first search algorithm for generating all maximal cliques of an undirected graph, in which pruning methods are employed as in the Bron–Kerbosch algorithm. All the maximal cliques generated are output in a tree-like form. Subsequently, we prove that its worst-case time complexity is O(3n/3) for an n-vertex graph. This is optimal as a function of n, since there exist up to 3n/3 maximal cliques in an n-vertex graph. The algorithm is also demonstrated to run very fast in practice by computational experiments.}, - pages = {28--42}, +@article{isella_whats_2011, + title = {What's in a crowd? Analysis of face-to-face behavioral networks}, + volume = {271}, + issn = {0022-5193}, + url = {http://www.sciencedirect.com/science/article/pii/S0022519310006284}, + doi = {10.1016/j.jtbi.2010.11.033}, + shorttitle = {What's in a crowd?}, + abstract = {The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing differences and similarities. We use our data to investigate the dynamics of a susceptible–infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks.}, + pages = {166--180}, number = {1}, - journaltitle = {Theoretical Computer Science}, - shortjournal = {Theoretical Computer Science}, - author = {Tomita, Etsuji and Tanaka, Akira and Takahashi, Haruhisa}, - urldate = {2018-07-31}, - date = {2006-10-25}, - keywords = {Computational experiments, Enumeration, Maximal cliques, Worst-case time complexity}, - file = {ScienceDirect Full Text PDF:/home/dimitri/Zotero/storage/QDLTAXHX/Tomita et al. - 2006 - The worst-case time complexity for generating all .pdf:application/pdf;ScienceDirect Snapshot:/home/dimitri/Zotero/storage/TCJ8J7MV/S0304397506003586.html:text/html} + journaltitle = {Journal of Theoretical Biology}, + shortjournal = {Journal of Theoretical Biology}, + author = {Isella, Lorenzo and Stehlé, Juliette and Barrat, Alain and Cattuto, Ciro and Pinton, Jean-François and Van den Broeck, Wouter}, + urldate = {2018-08-08}, + date = {2011-02-21}, + keywords = {Complex networks, Behavioral social networks, Dynamic networks, Face-to-face proximity, Information spreading}, + file = {Isella et al. - 2011 - What's in a crowd Analysis of face-to-face behavi.pdf:/home/dimitri/Zotero/storage/56DMKRM7/Isella et al. - 2011 - What's in a crowd Analysis of face-to-face behavi.pdf:application/pdf;ScienceDirect Snapshot:/home/dimitri/Zotero/storage/J4DJF3P8/S0022519310006284.html:text/html} +} + +@inproceedings{sulo_meaningful_2010, + location = {New York, {NY}, {USA}}, + title = {Meaningful Selection of Temporal Resolution for Dynamic Networks}, + isbn = {978-1-4503-0214-2}, + url = {http://doi.acm.org/10.1145/1830252.1830269}, + doi = {10.1145/1830252.1830269}, + series = {{MLG} '10}, + abstract = {The understanding of dynamics of data streams is greatly affected by the choice of temporal resolution at which the data are discretized, aggregated, and analyzed. Our paper focuses explicitly on data streams represented as dynamic networks. We propose a framework for identifying meaningful resolution levels that best reveal critical changes in the network structure, by balancing the reduction of noise with the loss of information. We demonstrate the applicability of our approach by analyzing various network statistics of both synthetic and real dynamic networks and using those to detect important events and changes in dynamic network structure.}, + pages = {127--136}, + booktitle = {Proceedings of the Eighth Workshop on Mining and Learning with Graphs}, + publisher = {{ACM}}, + author = {Sulo, Rajmonda and Berger-Wolf, Tanya and Grossman, Robert}, + urldate = {2018-08-22}, + date = {2010} +} + +@article{krings_effects_2012, + title = {Effects of time window size and placement on the structure of an aggregated communication network}, + volume = {1}, + rights = {2012 Krings et al.; licensee Springer.}, + issn = {2193-1127}, + url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds4}, + doi = {10.1140/epjds4}, + abstract = {Complex networks are often constructed by aggregating empirical data over time, such that a link represents the existence of interactions between the endpoint nodes and the link weight represents the intensity of such interactions within the aggregation time window. The resulting networks are then often considered static. More often than not, the aggregation time window is dictated by the availability of data, and the effects of its length on the resulting networks are rarely considered. Here, we address this question by studying the structural features of networks emerging from aggregating empirical data over different time intervals, focussing on networks derived from time-stamped, anonymized mobile telephone call records. Our results show that short aggregation intervals yield networks where strong links associated with dense clusters dominate; the seeds of such clusters or communities become already visible for intervals of around one week. The degree and weight distributions are seen to become stationary around a few days and a few weeks, respectively. An aggregation interval of around 30 days results in the stablest similar networks when consecutive windows are compared. For longer intervals, the effects of weak or random links become increasingly stronger, and the average degree of the network keeps growing even for intervals up to 180 days. The placement of the time window is also seen to affect the outcome: for short windows, different behavioural patterns play a role during weekends and weekdays, and for longer windows it is seen that networks aggregated during holiday periods are significantly different.}, + pages = {4}, + number = {1}, + journaltitle = {{EPJ} Data Science}, + author = {Krings, Gautier and Karsai, Márton and Bernhardsson, Sebastian and Blondel, Vincent D. and Saramäki, Jari}, + urldate = {2018-08-22}, + date = {2012-12}, + file = {Full Text PDF:/home/dimitri/Zotero/storage/3Y8AHZXA/Krings et al. - 2012 - Effects of time window size and placement on the s.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/AQLGJGDL/epjds4.html:text/html} +} + +@article{ribeiro_quantifying_2013, + title = {Quantifying the effect of temporal resolution on time-varying networks}, + volume = {3}, + rights = {2013 Nature Publishing Group}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/srep03006}, + doi = {10.1038/srep03006}, + abstract = {Time-varying networks describe a wide array of systems whose constituents and interactions evolve over time. They are defined by an ordered stream of interactions between nodes, yet they are often represented in terms of a sequence of static networks, each aggregating all edges and nodes present in a time interval of size Δt. In this work we quantify the impact of an arbitrary Δt on the description of a dynamical process taking place upon a time-varying network. We focus on the elementary random walk, and put forth a simple mathematical framework that well describes the behavior observed on real datasets. The analytical description of the bias introduced by time integrating techniques represents a step forward in the correct characterization of dynamical processes on time-varying graphs.}, + pages = {3006}, + journaltitle = {Scientific Reports}, + author = {Ribeiro, Bruno and Perra, Nicola and Baronchelli, Andrea}, + urldate = {2018-08-22}, + date = {2013-10-21}, + langid = {english}, + file = {Full Text PDF:/home/dimitri/Zotero/storage/9WPT9TVJ/Ribeiro et al. - 2013 - Quantifying the effect of temporal resolution on t.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/5IKE4WIN/srep03006.html:text/html} +} + +@book{fouss_algorithms_2016, + title = {Algorithms and Models for Network Data and Link Analysis}, + isbn = {978-1-107-12577-3}, + abstract = {Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. {MATLAB}®/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.}, + pagetotal = {549}, + publisher = {Cambridge University Press}, + author = {Fouss, François and Saerens, Marco and Shimbo, Masashi}, + date = {2016-07-12}, + langid = {english}, + note = {Google-Books-{ID}: {AUJfDAAAQBAJ}}, + keywords = {Computers / Computer Science, Computers / Databases / Data Mining, Computers / Databases / General}, + file = {Fouss et al. - 2016 - Algorithms and Models for Network Data and Link An.pdf:/home/dimitri/Zotero/storage/DULGH6PQ/Fouss et al. - 2016 - Algorithms and Models for Network Data and Link An.pdf:application/pdf} +} + +@article{cattuto_dynamics_2010, + title = {Dynamics of Person-to-Person Interactions from Distributed {RFID} Sensor Networks}, + volume = {5}, + issn = {1932-6203}, + url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0011596}, + doi = {10.1371/journal.pone.0011596}, + abstract = {Background Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. Methods and Findings We use active Radio Frequency Identification ({RFID}) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Conclusions Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.}, + pages = {e11596}, + number = {7}, + journaltitle = {{PLOS} {ONE}}, + shortjournal = {{PLOS} {ONE}}, + author = {Cattuto, Ciro and Broeck, Wouter Van den and Barrat, Alain and Colizza, Vittoria and Pinton, Jean-François and Vespignani, Alessandro}, + urldate = {2018-09-07}, + date = {2010-07-15}, + langid = {english}, + keywords = {Computer networks, Behavior, Behavioral geography, Human mobility, Probability distribution, Radio waves, Statistical data, Statistical distributions}, + file = {Full Text PDF:/home/dimitri/Zotero/storage/GFAHQ6F2/Cattuto et al. - 2010 - Dynamics of Person-to-Person Interactions from Dis.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/67R2UX2N/article.html:text/html} +} + +@online{noauthor_infectious_2011, + title = {Infectious {SocioPatterns}}, + url = {http://www.sociopatterns.org/datasets/infectious-sociopatterns/}, + abstract = {A research project that aims to uncover fundamental patterns in social dynamics and coordinated human activity through a data-driven approach.}, + titleaddon = {{SocioPatterns}.org}, + date = {2011-03-31}, + langid = {american}, + file = {Snapshot:/home/dimitri/Zotero/storage/VNBHGW9K/infectious-sociopatterns.html:text/html} +} + +@online{noauthor_infectious_2011-1, + title = {Infectious {SocioPatterns} dynamic contact networks}, + url = {http://www.sociopatterns.org/datasets/infectious-sociopatterns-dynamic-contact-networks/}, + abstract = {A research project that aims to uncover fundamental patterns in social dynamics and coordinated human activity through a data-driven approach.}, + titleaddon = {{SocioPatterns}.org}, + date = {2011-11-28}, + langid = {american}, + file = {Snapshot:/home/dimitri/Zotero/storage/9YMG2VGK/infectious-sociopatterns-dynamic-contact-networks.html:text/html} +} + +@article{holme_attack_2002, + title = {Attack vulnerability of complex networks}, + volume = {65}, + url = {https://link.aps.org/doi/10.1103/PhysRevE.65.056109}, + doi = {10.1103/PhysRevE.65.056109}, + abstract = {We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.}, + pages = {056109}, + number = {5}, + journaltitle = {Physical Review E}, + shortjournal = {Phys. Rev. E}, + author = {Holme, Petter and Kim, Beom Jun and Yoon, Chang No and Han, Seung Kee}, + urldate = {2018-09-09}, + date = {2002-05-07}, + file = {APS Snapshot:/home/dimitri/Zotero/storage/PW8XHWT3/PhysRevE.65.html:text/html} +} + +@article{aledavood_digital_2015, + title = {Digital daily cycles of individuals}, + volume = {3}, + issn = {2296-424X}, + url = {https://www.frontiersin.org/articles/10.3389/fphy.2015.00073/full}, + doi = {10.3389/fphy.2015.00073}, + abstract = {Humans, like almost all animals, are phase-locked to the diurnal cycle. Most of us sleep at night and are active through the day. Because we have evolved to function with this cycle, the circadian rhythm is deeply ingrained and even detectable at the biochemical level. However, within the broader day-night pattern, there are individual differences: e.g., some of us are intrinsically morning-active, while others prefer evenings. In this article, we look at digital daily cycles: circadian patterns of activity viewed through the lens of auto-recorded data of communication and online activity. We begin at the aggregate level, discuss earlier results, and illustrate differences between population-level daily rhythms in different media. Then we move on to the individual level, and show that there is a strong individual-level variation beyond averages: individuals typically have their distinctive daily pattern that persists in time. We conclude by discussing the driving forces behind these signature daily patterns, from personal traits (morningness/eveningness) to variation in activity level and external constraints, and outline possibilities for future research.}, + journaltitle = {Frontiers in Physics}, + shortjournal = {Front. Phys.}, + author = {Aledavood, Talayeh and Lehmann, Sune and Saramäki, Jari}, + urldate = {2018-09-09}, + date = {2015}, + keywords = {circadian rhythms, Digital phenotyping, electronic communication records, individual differences, Mobile phones}, + file = {Full Text PDF:/home/dimitri/Zotero/storage/TZP4KMJ4/Aledavood et al. - 2015 - Digital daily cycles of individuals.pdf:application/pdf} +} + +@article{aledavood_daily_2015, + title = {Daily Rhythms in Mobile Telephone Communication}, + volume = {10}, + issn = {1932-6203}, + url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138098}, + doi = {10.1371/journal.pone.0138098}, + abstract = {Circadian rhythms are known to be important drivers of human activity and the recent availability of electronic records of human behaviour has provided fine-grained data of temporal patterns of activity on a large scale. Further, questionnaire studies have identified important individual differences in circadian rhythms, with people broadly categorised into morning-like or evening-like individuals. However, little is known about the social aspects of these circadian rhythms, or how they vary across individuals. In this study we use a unique 18-month dataset that combines mobile phone calls and questionnaire data to examine individual differences in the daily rhythms of mobile phone activity. We demonstrate clear individual differences in daily patterns of phone calls, and show that these individual differences are persistent despite a high degree of turnover in the individuals’ social networks. Further, women’s calls were longer than men’s calls, especially during the evening and at night, and these calls were typically focused on a small number of emotionally intense relationships. These results demonstrate that individual differences in circadian rhythms are not just related to broad patterns of morningness and eveningness, but have a strong social component, in directing phone calls to specific individuals at specific times of day.}, + pages = {e0138098}, + number = {9}, + journaltitle = {{PLOS} {ONE}}, + shortjournal = {{PLOS} {ONE}}, + author = {Aledavood, Talayeh and López, Eduardo and Roberts, Sam G. B. and Reed-Tsochas, Felix and Moro, Esteban and Dunbar, Robin I. M. and Saramäki, Jari}, + urldate = {2018-09-09}, + date = {2015-09-21}, + langid = {english}, + keywords = {Behavior, Cell phones, Circadian rhythms, Emotions, Entropy, Interpersonal relationships, Questionnaires, Social networks}, + file = {Full Text PDF:/home/dimitri/Zotero/storage/FFG9S8PK/Aledavood et al. - 2015 - Daily Rhythms in Mobile Telephone Communication.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/MI9VN585/article.html:text/html} +} + +@article{holme_network_2003, + title = {Network dynamics of ongoing social relationships}, + volume = {64}, + issn = {0295-5075}, + url = {http://iopscience.iop.org/article/10.1209/epl/i2003-00505-4/meta}, + doi = {10.1209/epl/i2003-00505-4}, + pages = {427}, + number = {3}, + journaltitle = {{EPL} (Europhysics Letters)}, + shortjournal = {{EPL}}, + author = {Holme, P.}, + urldate = {2018-09-09}, + date = {2003-11}, + langid = {english}, + file = {Snapshot:/home/dimitri/Zotero/storage/5IXF7A2B/meta.html:text/html} +} + +@article{jo_circadian_2012, + title = {Circadian pattern and burstiness in mobile phone communication}, + volume = {14}, + issn = {1367-2630}, + url = {http://stacks.iop.org/1367-2630/14/i=1/a=013055?key=crossref.49fc43f1e121d47657c8da6f05484442}, + doi = {10.1088/1367-2630/14/1/013055}, + pages = {013055}, + number = {1}, + journaltitle = {New Journal of Physics}, + author = {Jo, Hang-Hyun and Karsai, Márton and Kertész, János and Kaski, Kimmo}, + urldate = {2018-09-09}, + date = {2012-01-25} } \ No newline at end of file diff --git a/dissertation/wrcf.py b/dissertation/wrcf.py new file mode 100644 index 0000000..77d2b25 --- /dev/null +++ b/dissertation/wrcf.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python3 + +import numpy as np +import igraph as ig +import dionysus as d + + +def wrcf(G, weight="weight"): + """Compute the weight-rank clique filtration (WRCF) of a graph. + + :param G: igraph Graph + :param weight: name of the weight attribute + + :return: a Dionysus filtration. + """ + # Define filtration step 0 as the set of all nodes + filt = d.Filtration() + for v in G.vs: + filt.append(d.Simplex([v.index], 0)) + # Rank all edge weights + distinct_weights = np.unique(G.es[weight])[::-1] + for t, w in enumerate(distinct_weights): + # At filtration step t, threshold the graph at weight[t] + subg = G.subgraph_edges(G.es(lambda e: e[weight] >= w)) + # Find all maximal cliques and define them to be simplices + for clique in subg.maximal_cliques(): + for s in d.closure([d.Simplex(clique)], len(clique)): + filt.append(d.Simplex(s, t+1)) + filt.sort() + return(filt) + + +def wrcf_diagram(graph, weight="weight"): + """Compute persistence diagrams of a graph using WRCF. + + :param graph: igraph Graph + :param weight: name of the weight attribute + + :return: a list of persistence diagrams. + """ + filt = wrcf(graph, weight=weight) + pers = d.homology_persistence(filt) + dgms = d.init_diagrams(pers, filt) + return dgms diff --git a/dissertation/zigzag.py b/dissertation/zigzag.py new file mode 100644 index 0000000..4692028 --- /dev/null +++ b/dissertation/zigzag.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 + +import numpy as np +import igraph as ig +import dionysus as d + + +def sliding_windows(g, res=0.1, overlap=0): + """Compute subnetworks of a temporal network based on temporal + partitioning of the time range. + + :param g: igraph Graph + :param res: resolution + :param overlap: overlap + + :return: a list of temporal networks. + """ + times = np.array(g.es["time"]) + duration = res * (times.max() - times.min()) + windows = [] + for i in range(int(1/res)): + edges = g.es.select(time_gt=times.min() + duration*i, + time_lt=times.min() + duration*(i+1)) + windows.append(g.subgraph_edges(edges)) + return windows + + +def max_simplicial_complex(g): + """Return the maximal simplicial complex of a network g. + """ + return d.Filtration(g.maximal_cliques()) + + +def find_transitions(a): + """Find the transition times in an array of presence times. + """ + res = [] + prev = False + for i, cur in enumerate(a): + if cur != prev: + res.append(i) + prev = cur + return res + + +def presence_times(g): + """Compute the data required to compute zigzag persistence: + simplicial complex and transition times. + + :param g: igraph Graph + + :return: a tuple with the maximum simplicial complex and the + transition times of each simplex. + """ + max_simplicial_complex = d.Filtration(g.cliques()) + filts = [] + for t in np.sort(np.unique(g.es["time"])): + edges = g.es.select(time_eq=t) + cliques = g.subgraph_edges(edges).cliques() + filts.append(d.Filtration(cliques)) + presences = [[s in filt for filt in filts] for s in max_simplicial_complex] + presences = [find_transitions(p) for p in presences] + return (max_simplicial_complex, presences) + + +def zigzag_network(g): + """Compute zigzag persistence on a temporal network. + + :param g: igraph Graph + + :return: a list of persistence diagrams. + """ + (f, t) = presence_times(g) + _, dgms, _ = d.zigzag_homology_persistence(f, t) + return dgms