Dissertation: final update
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Dimitri Lozeve
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@article{girvan_community_2002,
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title = {Community structure in social and biological networks},
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volume = {99},
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issn = {0027-8424},
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url = {http://arxiv.org/abs/cond-mat/0112110},
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doi = {10.1073/pnas.},
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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.},
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pages = {7821--7826},
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number = {12},
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journaltitle = {Proceedings of the National Academy of Sciences of United States of America},
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author = {Girvan, M. and Newman, M. E. J},
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date = {2002},
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pmid = {12060727},
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keywords = {Models, Neural Networks (Computer), Theoretical, Algorithms, Animals, Community Networks, Computer Simulation, Humans, Nerve Net, Nerve Net: physiology, Social Behavior},
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file = {Attachment:/home/dimitri/Zotero/storage/X3C73Q5I/Girvan, Newman - 2002 - Community structure in social and biological networks.pdf:application/pdf}
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}
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@book{newman_networks:_2010,
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location = {Oxford ; New York},
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title = {Networks: an introduction},
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isbn = {978-0-19-920665-0},
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shorttitle = {Networks},
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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"--},
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pagetotal = {772},
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publisher = {Oxford University Press},
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author = {Newman, M. E. J.},
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date = {2010},
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note = {{OCLC}: ocn456837194},
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keywords = {Engineering systems, Network analysis (Planning), Social systems, System analysis, Systems biology},
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file = {Mark_Newman_Networks_An_Introduction.pdf:/home/dimitri/Zotero/storage/FDMM48IV/Mark_Newman_Networks_An_Introduction.pdf:application/pdf}
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}
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@article{tabourier_predicting_2016,
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title = {Predicting links in ego-networks using temporal information},
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volume = {5},
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@ -425,21 +457,206 @@
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file = {Snapshot:/home/dimitri/Zotero/storage/CYSLT5MA/10.html:text/html}
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}
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@article{tomita_worst-case_2006,
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title = {The worst-case time complexity for generating all maximal cliques and computational experiments},
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volume = {363},
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issn = {0304-3975},
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url = {http://www.sciencedirect.com/science/article/pii/S0304397506003586},
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doi = {10.1016/j.tcs.2006.06.015},
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series = {Computing and Combinatorics},
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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.},
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pages = {28--42},
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@article{isella_whats_2011,
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title = {What's in a crowd? Analysis of face-to-face behavioral networks},
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volume = {271},
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issn = {0022-5193},
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url = {http://www.sciencedirect.com/science/article/pii/S0022519310006284},
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doi = {10.1016/j.jtbi.2010.11.033},
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shorttitle = {What's in a crowd?},
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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.},
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pages = {166--180},
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number = {1},
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journaltitle = {Theoretical Computer Science},
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shortjournal = {Theoretical Computer Science},
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author = {Tomita, Etsuji and Tanaka, Akira and Takahashi, Haruhisa},
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urldate = {2018-07-31},
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date = {2006-10-25},
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keywords = {Computational experiments, Enumeration, Maximal cliques, Worst-case time complexity},
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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}
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journaltitle = {Journal of Theoretical Biology},
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shortjournal = {Journal of Theoretical Biology},
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author = {Isella, Lorenzo and Stehlé, Juliette and Barrat, Alain and Cattuto, Ciro and Pinton, Jean-François and Van den Broeck, Wouter},
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urldate = {2018-08-08},
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date = {2011-02-21},
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keywords = {Complex networks, Behavioral social networks, Dynamic networks, Face-to-face proximity, Information spreading},
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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}
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}
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@inproceedings{sulo_meaningful_2010,
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location = {New York, {NY}, {USA}},
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title = {Meaningful Selection of Temporal Resolution for Dynamic Networks},
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isbn = {978-1-4503-0214-2},
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url = {http://doi.acm.org/10.1145/1830252.1830269},
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doi = {10.1145/1830252.1830269},
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series = {{MLG} '10},
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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.},
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pages = {127--136},
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booktitle = {Proceedings of the Eighth Workshop on Mining and Learning with Graphs},
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publisher = {{ACM}},
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author = {Sulo, Rajmonda and Berger-Wolf, Tanya and Grossman, Robert},
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urldate = {2018-08-22},
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date = {2010}
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}
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@article{krings_effects_2012,
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title = {Effects of time window size and placement on the structure of an aggregated communication network},
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volume = {1},
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rights = {2012 Krings et al.; licensee Springer.},
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issn = {2193-1127},
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url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds4},
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doi = {10.1140/epjds4},
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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.},
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pages = {4},
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number = {1},
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journaltitle = {{EPJ} Data Science},
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author = {Krings, Gautier and Karsai, Márton and Bernhardsson, Sebastian and Blondel, Vincent D. and Saramäki, Jari},
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urldate = {2018-08-22},
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date = {2012-12},
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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}
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}
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@article{ribeiro_quantifying_2013,
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title = {Quantifying the effect of temporal resolution on time-varying networks},
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volume = {3},
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rights = {2013 Nature Publishing Group},
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issn = {2045-2322},
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url = {https://www.nature.com/articles/srep03006},
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doi = {10.1038/srep03006},
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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.},
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pages = {3006},
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journaltitle = {Scientific Reports},
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author = {Ribeiro, Bruno and Perra, Nicola and Baronchelli, Andrea},
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urldate = {2018-08-22},
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date = {2013-10-21},
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langid = {english},
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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}
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}
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@book{fouss_algorithms_2016,
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title = {Algorithms and Models for Network Data and Link Analysis},
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isbn = {978-1-107-12577-3},
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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.},
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pagetotal = {549},
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publisher = {Cambridge University Press},
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author = {Fouss, François and Saerens, Marco and Shimbo, Masashi},
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date = {2016-07-12},
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langid = {english},
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note = {Google-Books-{ID}: {AUJfDAAAQBAJ}},
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keywords = {Computers / Computer Science, Computers / Databases / Data Mining, Computers / Databases / General},
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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}
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}
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@article{cattuto_dynamics_2010,
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title = {Dynamics of Person-to-Person Interactions from Distributed {RFID} Sensor Networks},
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volume = {5},
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issn = {1932-6203},
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url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0011596},
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doi = {10.1371/journal.pone.0011596},
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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.},
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pages = {e11596},
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number = {7},
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journaltitle = {{PLOS} {ONE}},
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shortjournal = {{PLOS} {ONE}},
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author = {Cattuto, Ciro and Broeck, Wouter Van den and Barrat, Alain and Colizza, Vittoria and Pinton, Jean-François and Vespignani, Alessandro},
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urldate = {2018-09-07},
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date = {2010-07-15},
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langid = {english},
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keywords = {Computer networks, Behavior, Behavioral geography, Human mobility, Probability distribution, Radio waves, Statistical data, Statistical distributions},
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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}
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}
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@online{noauthor_infectious_2011,
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title = {Infectious {SocioPatterns}},
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url = {http://www.sociopatterns.org/datasets/infectious-sociopatterns/},
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abstract = {A research project that aims to uncover fundamental patterns in social dynamics and coordinated human activity through a data-driven approach.},
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titleaddon = {{SocioPatterns}.org},
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date = {2011-03-31},
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langid = {american},
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file = {Snapshot:/home/dimitri/Zotero/storage/VNBHGW9K/infectious-sociopatterns.html:text/html}
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}
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@online{noauthor_infectious_2011-1,
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title = {Infectious {SocioPatterns} dynamic contact networks},
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url = {http://www.sociopatterns.org/datasets/infectious-sociopatterns-dynamic-contact-networks/},
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abstract = {A research project that aims to uncover fundamental patterns in social dynamics and coordinated human activity through a data-driven approach.},
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titleaddon = {{SocioPatterns}.org},
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date = {2011-11-28},
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langid = {american},
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file = {Snapshot:/home/dimitri/Zotero/storage/9YMG2VGK/infectious-sociopatterns-dynamic-contact-networks.html:text/html}
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}
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@article{holme_attack_2002,
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title = {Attack vulnerability of complex networks},
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volume = {65},
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url = {https://link.aps.org/doi/10.1103/PhysRevE.65.056109},
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doi = {10.1103/PhysRevE.65.056109},
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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.},
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pages = {056109},
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number = {5},
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journaltitle = {Physical Review E},
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shortjournal = {Phys. Rev. E},
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author = {Holme, Petter and Kim, Beom Jun and Yoon, Chang No and Han, Seung Kee},
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urldate = {2018-09-09},
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date = {2002-05-07},
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file = {APS Snapshot:/home/dimitri/Zotero/storage/PW8XHWT3/PhysRevE.65.html:text/html}
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}
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@article{aledavood_digital_2015,
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title = {Digital daily cycles of individuals},
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volume = {3},
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issn = {2296-424X},
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url = {https://www.frontiersin.org/articles/10.3389/fphy.2015.00073/full},
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doi = {10.3389/fphy.2015.00073},
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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.},
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journaltitle = {Frontiers in Physics},
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shortjournal = {Front. Phys.},
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author = {Aledavood, Talayeh and Lehmann, Sune and Saramäki, Jari},
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urldate = {2018-09-09},
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date = {2015},
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keywords = {circadian rhythms, Digital phenotyping, electronic communication records, individual differences, Mobile phones},
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file = {Full Text PDF:/home/dimitri/Zotero/storage/TZP4KMJ4/Aledavood et al. - 2015 - Digital daily cycles of individuals.pdf:application/pdf}
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}
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@article{aledavood_daily_2015,
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title = {Daily Rhythms in Mobile Telephone Communication},
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volume = {10},
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issn = {1932-6203},
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url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138098},
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doi = {10.1371/journal.pone.0138098},
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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.},
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pages = {e0138098},
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number = {9},
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journaltitle = {{PLOS} {ONE}},
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shortjournal = {{PLOS} {ONE}},
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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},
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urldate = {2018-09-09},
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date = {2015-09-21},
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langid = {english},
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keywords = {Behavior, Cell phones, Circadian rhythms, Emotions, Entropy, Interpersonal relationships, Questionnaires, Social networks},
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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}
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}
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@article{holme_network_2003,
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title = {Network dynamics of ongoing social relationships},
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volume = {64},
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issn = {0295-5075},
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url = {http://iopscience.iop.org/article/10.1209/epl/i2003-00505-4/meta},
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doi = {10.1209/epl/i2003-00505-4},
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pages = {427},
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number = {3},
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journaltitle = {{EPL} (Europhysics Letters)},
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shortjournal = {{EPL}},
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author = {Holme, P.},
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urldate = {2018-09-09},
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date = {2003-11},
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langid = {english},
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file = {Snapshot:/home/dimitri/Zotero/storage/5IXF7A2B/meta.html:text/html}
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}
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@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}
|
||||
}
|
||||
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