662 lines
No EOL
71 KiB
BibTeX
662 lines
No EOL
71 KiB
BibTeX
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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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rights = {2016 Tabourier et al.},
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issn = {2193-1127},
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url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0062-0},
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doi = {10.1140/epjds/s13688-015-0062-0},
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abstract = {Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network. In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships. As the structural information is very poor, we rely on another source of information to predict links among egos’ neighbors: the timing of interactions. We define several features to capture different kinds of temporal information and apply machine learning methods to combine these various features and improve the quality of the prediction. We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features which prove themselves to perform well in this context, in particular the temporal profile of interactions and elapsed time between contacts.},
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pages = {1},
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number = {1},
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journaltitle = {{EPJ} Data Science},
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author = {Tabourier, Lionel and Libert, Anne-Sophie and Lambiotte, Renaud},
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urldate = {2018-02-13},
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date = {2016-12},
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file = {Full Text PDF:/home/dimitri/Zotero/storage/ETM66HPY/Tabourier et al. - 2016 - Predicting links in ego-networks using temporal in.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/IUNKJ9YF/s13688-015-0062-0.html:text/html}
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}
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@article{kivela_multilayer_2014,
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title = {Multilayer Networks},
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volume = {2},
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issn = {2051-1310, 2051-1329},
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url = {http://arxiv.org/abs/1309.7233},
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doi = {10.1093/comnet/cnu016},
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abstract = {In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.},
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pages = {203--271},
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number = {3},
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journaltitle = {Journal of Complex Networks},
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author = {Kivelä, Mikko and Arenas, Alexandre and Barthelemy, Marc and Gleeson, James P. and Moreno, Yamir and Porter, Mason A.},
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urldate = {2018-02-13},
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date = {2014-09-01},
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eprinttype = {arxiv},
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eprint = {1309.7233},
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keywords = {Physics - Physics and Society, Computer Science - Social and Information Networks},
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file = {arXiv\:1309.7233 PDF:/home/dimitri/Zotero/storage/F98JFB2E/Kivelä et al. - 2014 - Multilayer Networks.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/7WBJRIBQ/1309.html:text/html}
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}
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@article{porter_dynamical_2014,
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title = {Dynamical Systems on Networks: A Tutorial},
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url = {http://arxiv.org/abs/1403.7663},
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shorttitle = {Dynamical Systems on Networks},
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abstract = {We give a tutorial for the study of dynamical systems on networks. We focus especially on "simple" situations that are tractable analytically, because they can be very insightful and provide useful springboards for the study of more complicated scenarios. We briefly motivate why examining dynamical systems on networks is interesting and important, and we then give several fascinating examples and discuss some theoretical results. We also briefly discuss dynamical systems on dynamical (i.e., time-dependent) networks, overview software implementations, and give an outlook on the field.},
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journaltitle = {{arXiv}:1403.7663 [cond-mat, physics:nlin, physics:physics]},
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author = {Porter, Mason A. and Gleeson, James P.},
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urldate = {2018-02-13},
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date = {2014-03-29},
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eprinttype = {arxiv},
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eprint = {1403.7663},
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keywords = {Physics - Physics and Society, Computer Science - Social and Information Networks, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics, Nonlinear Sciences - Adaptation and Self-Organizing Systems},
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file = {arXiv\:1403.7663 PDF:/home/dimitri/Zotero/storage/XBRAHARB/Porter and Gleeson - 2014 - Dynamical Systems on Networks A Tutorial.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/LF7GCTFE/1403.html:text/html}
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}
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@article{casteigts_time-varying_2012,
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title = {Time-varying graphs and dynamic networks},
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volume = {27},
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issn = {1744-5760},
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url = {https://doi.org/10.1080/17445760.2012.668546},
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doi = {10.1080/17445760.2012.668546},
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abstract = {The past few years have seen intensive research efforts carried out in some apparently unrelated areas of dynamic systems – delay-tolerant networks, opportunistic-mobility networks and social networks – obtaining closely related insights. Indeed, the concepts discovered in these investigations can be viewed as parts of the same conceptual universe, and the formal models proposed so far to express some specific concepts are the components of a larger formal description of this universe. The main contribution of this paper is to integrate the vast collection of concepts, formalisms and results found in the literature into a unified framework, which we call time-varying graphs ({TVGs}). Using this framework, it is possible to express directly in the same formalism not only the concepts common to all those different areas, but also those specific to each. Based on this definitional work, employing both existing results and original observations, we present a hierarchical classification of {TVGs}; each class corresponds to a significant property examined in the distributed computing literature. We then examine how {TVGs} can be used to study the evolution of network properties, and propose different techniques, depending on whether the indicators for these properties are atemporal (as in the majority of existing studies) or temporal. Finally, we briefly discuss the introduction of randomness in {TVGs}.},
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pages = {387--408},
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number = {5},
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journaltitle = {International Journal of Parallel, Emergent and Distributed Systems},
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author = {Casteigts, Arnaud and Flocchini, Paola and Quattrociocchi, Walter and Santoro, Nicola},
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urldate = {2018-02-21},
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date = {2012-10-01},
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keywords = {social networks, delay-tolerant networks, distributed computing, dynamic graphs, opportunistic networks, time-varying graphs},
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file = {1012.0009.pdf:/home/dimitri/Zotero/storage/IPW9FMKH/1012.0009.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/R94NRJG7/17445760.2012.html:text/html}
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}
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@article{kuhn_dynamic_2011,
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title = {Dynamic Networks: Models and Algorithms},
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volume = {42},
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issn = {0163-5700},
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url = {http://doi.acm.org/10.1145/1959045.1959064},
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doi = {10.1145/1959045.1959064},
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shorttitle = {Dynamic Networks},
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pages = {82--96},
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number = {1},
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journaltitle = {{SIGACT} News},
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author = {Kuhn, Fabian and Oshman, Rotem},
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urldate = {2018-02-21},
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date = {2011-03},
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file = {kuhn2011.pdf:/home/dimitri/Zotero/storage/WEN85Y2C/kuhn2011.pdf:application/pdf}
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}
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@article{michail_introduction_2016,
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title = {An Introduction to Temporal Graphs: An Algorithmic Perspective},
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volume = {12},
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issn = {1542-7951},
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url = {https://doi.org/10.1080/15427951.2016.1177801},
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doi = {10.1080/15427951.2016.1177801},
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shorttitle = {An Introduction to Temporal Graphs},
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abstract = {A temporal graph is, informally speaking, a graph that changes with time. When time is discrete and only the relationships between the participating entities may change and not the entities themselves, a temporal graph may be viewed as a sequence G1, G2…, Gl of static graphs over the same (static) set of nodes V. Though static graphs have been extensively studied, for their temporal generalization we are still far from having a concrete set of structural and algorithmic principles. Recent research shows that many graph properties and problems become radically different and usually substantially more difficult when an extra time dimension is added to them. Moreover, there is already a rich and rapidly growing set of modern systems and applications that can be naturally modeled and studied via temporal graphs. This, further motivates the need for the development of a temporal extension of graph theory. We survey here recent results on temporal graphs and temporal graph problems that have appeared in the Computer Science community.},
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pages = {239--280},
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number = {4},
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journaltitle = {Internet Mathematics},
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author = {Michail, Othon},
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urldate = {2018-02-21},
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date = {2016-07-03},
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file = {1503.00278.pdf:/home/dimitri/Zotero/storage/QQU5QN6M/1503.00278.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/A9KBYDWN/15427951.2016.html:text/html}
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}
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@article{kempe_connectivity_2002,
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title = {Connectivity and Inference Problems for Temporal Networks},
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volume = {64},
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issn = {0022-0000},
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url = {http://www.sciencedirect.com/science/article/pii/S0022000002918295},
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doi = {10.1006/jcss.2002.1829},
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abstract = {Many network problems are based on fundamental relationships involving time. Consider, for example, the problems of modeling the flow of information through a distributed network, studying the spread of a disease through a population, or analyzing the reachability properties of an airline timetable. In such settings, a natural model is that of a graph in which each edge is annotated with a time label specifying the time at which its endpoints “communicated.” We will call such a graph a temporal network. To model the notion that information in such a network “flows” only on paths whose labels respect the ordering of time, we call a path time-respecting if the time labels on its edges are non-decreasing. The central motivation for our work is the following question: how do the basic combinatorial and algorithmic properties of graphs change when we impose this additional temporal condition? The notion of a path is intrinsic to many of the most fundamental algorithmic problems on graphs; spanning trees, connectivity, flows, and cuts are some examples. When we focus on time-respecting paths in place of arbitrary paths, many of these problems acquire a character that is different from the traditional setting, but very rich in its own right. We provide results on two types of problems for temporal networks. First, we consider connectivity problems, in which we seek disjoint time-respecting paths between pairs of nodes. The natural analogue of Menger's Theorem for node-disjoint paths fails in general for time-respecting paths; we give a non-trivial characterization of those graphs for which the theorem does hold in terms of an excluded subdivision theorem, and provide a polynomial-time algorithm for connectivity on this class of graphs. (The problem on general graphs is {NP}-complete.) We then define and study the class of inference problems, in which we seek to reconstruct a partially specified time labeling of a network in a manner consistent with an observed history of information flow.},
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pages = {820--842},
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number = {4},
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journaltitle = {Journal of Computer and System Sciences},
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shortjournal = {Journal of Computer and System Sciences},
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author = {Kempe, David and Kleinberg, Jon and Kumar, Amit},
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urldate = {2018-02-22},
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date = {2002-06-01},
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file = {10.1.1.30.6741.pdf:/home/dimitri/Zotero/storage/I9CR9UGA/10.1.1.30.6741.pdf:application/pdf;ScienceDirect Snapshot:/home/dimitri/Zotero/storage/87E98N2I/S0022000002918295.html:text/html}
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}
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@inproceedings{mertzios_temporal_2013,
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title = {Temporal Network Optimization Subject to Connectivity Constraints},
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isbn = {978-3-642-39211-5 978-3-642-39212-2},
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url = {https://link.springer.com/chapter/10.1007/978-3-642-39212-2_57},
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doi = {10.1007/978-3-642-39212-2_57},
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series = {Lecture Notes in Computer Science},
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abstract = {In this work we consider temporal networks, i.e. networks defined by a labeling λ assigning to each edge of an underlying graph G a set of discrete time-labels. The labels of an edge, which are natural numbers, indicate the discrete time moments at which the edge is available. We focus on path problems of temporal networks. In particular, we consider time-respecting paths, i.e. paths whose edges are assigned by λ a strictly increasing sequence of labels. We begin by giving two efficient algorithms for computing shortest time-respecting paths on a temporal network. We then prove that there is a natural analogue of Menger’s theorem holding for arbitrary temporal networks. Finally, we propose two cost minimization parameters for temporal network design. One is the temporality of G, in which the goal is to minimize the maximum number of labels of an edge, and the other is the temporal cost of G, in which the goal is to minimize the total number of labels used. Optimization of these parameters is performed subject to some connectivity constraint. We prove several lower and upper bounds for the temporality and the temporal cost of some very basic graph families such as rings, directed acyclic graphs, and trees.},
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eventtitle = {International Colloquium on Automata, Languages, and Programming},
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pages = {657--668},
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booktitle = {Automata, Languages, and Programming},
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publisher = {Springer, Berlin, Heidelberg},
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author = {Mertzios, George B. and Michail, Othon and Chatzigiannakis, Ioannis and Spirakis, Paul G.},
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urldate = {2018-02-22},
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date = {2013-07-08},
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langid = {english},
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file = {1502.04382.pdf:/home/dimitri/Zotero/storage/AUZGZX8M/1502.04382.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/8AUNJDZ2/978-3-642-39212-2_57.html:text/html}
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}
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@article{akrida_ephemeral_2016,
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title = {Ephemeral networks with random availability of links: The case of fast networks},
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volume = {87},
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issn = {0743-7315},
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url = {http://www.sciencedirect.com/science/article/pii/S0743731515001872},
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doi = {10.1016/j.jpdc.2015.10.002},
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shorttitle = {Ephemeral networks with random availability of links},
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abstract = {We consider here a model of temporal networks, the links of which are available only at certain moments in time, chosen randomly from a subset of the positive integers. We define the notion of the Temporal Diameter of such networks. We also define fast and slow such temporal networks with respect to the expected value of their temporal diameter. We then provide a partial characterization of fast random temporal networks. We also define the critical availability as a measure of periodic random availability of the links of a network, required to make the network fast. We finally give a lower bound as well as an upper bound on the (critical) availability.},
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pages = {109--120},
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journaltitle = {Journal of Parallel and Distributed Computing},
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shortjournal = {Journal of Parallel and Distributed Computing},
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author = {Akrida, Eleni C. and Gąsieniec, Leszek and Mertzios, George B. and Spirakis, Paul G.},
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urldate = {2018-02-22},
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date = {2016-01-01},
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keywords = {Availability, Diameter, Random input, Temporal networks},
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file = {10.1.1.721.6341.pdf:/home/dimitri/Zotero/storage/RJU2GI5T/10.1.1.721.6341.pdf:application/pdf;ScienceDirect Snapshot:/home/dimitri/Zotero/storage/6NLW8PWX/S0743731515001872.html:text/html}
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}
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@article{benson_simplicial_2018,
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title = {Simplicial Closure and Higher-order Link Prediction},
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url = {http://arxiv.org/abs/1802.06916},
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abstract = {Networks provide a powerful formalism for modeling complex systems, by representing the underlying set of pairwise interactions. But much of the structure within these systems involves interactions that take place among more than two nodes at once; for example, communication within a group rather than person-to-person, collaboration among a team rather than a pair of co-authors, or biological interaction between a set of molecules rather than just two. We refer to these type of simultaneous interactions on sets of more than two nodes as higher-order interactions; they are ubiquitous, but the empirical study of them has lacked a general framework for evaluating higher-order models. Here we introduce such a framework, based on link prediction, a fundamental problem in network analysis. The traditional link prediction problem seeks to predict the appearance of new links in a network, and here we adapt it to predict which (larger) sets of elements will have future interactions. We study the temporal evolution of 19 datasets from a variety of domains, and use our higher-order formulation of link prediction to assess the types of structural features that are most predictive of new multi-way interactions. Among our results, we find that different domains vary considerably in their distribution of higher-order structural parameters, and that the higher-order link prediction problem exhibits some fundamental differences from traditional pairwise link prediction, with a greater role for local rather than long-range information in predicting the appearance of new interactions.},
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journaltitle = {{arXiv}:1802.06916 [cond-mat, physics:physics, stat]},
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author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon},
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urldate = {2018-02-27},
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date = {2018-02-19},
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eprinttype = {arxiv},
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eprint = {1802.06916},
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keywords = {Statistics - Machine Learning, Mathematics - Algebraic Topology, Physics - Physics and Society, Computer Science - Social and Information Networks, Condensed Matter - Statistical Mechanics},
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file = {arXiv\:1802.06916 PDF:/home/dimitri/Zotero/storage/C5IG7QGL/Benson et al. - 2018 - Simplicial Closure and Higher-order Link Predictio.pdf:application/pdf}
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}
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@article{mellor_temporal_2017,
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title = {The Temporal Event Graph},
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issn = {2051-1310, 2051-1329},
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url = {http://arxiv.org/abs/1706.02128},
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doi = {10.1093/comnet/cnx048},
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abstract = {Temporal networks are increasingly being used to model the interactions of complex systems. Most studies require the temporal aggregation of edges (or events) into discrete time steps to perform analysis. In this article we describe a static, lossless, and unique representation of a temporal network, the temporal event graph ({TEG}). The {TEG} describes the temporal network in terms of both the inter-event time and two-event temporal motif distributions. By considering these distributions in unison we provide a new method to characterise the behaviour of individuals and collectives in temporal networks as well as providing a natural decomposition of the network. We illustrate the utility of the {TEG} by providing examples on both synthetic and real temporal networks.},
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journaltitle = {Journal of Complex Networks},
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author = {Mellor, Andrew},
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urldate = {2018-02-27},
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date = {2017-10-06},
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eprinttype = {arxiv},
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eprint = {1706.02128},
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keywords = {Physics - Physics and Society, Computer Science - Social and Information Networks, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Physics - Data Analysis, Statistics and Probability},
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file = {arXiv\:1706.02128 PDF:/home/dimitri/Zotero/storage/6HQ7IV56/Mellor - 2017 - The Temporal Event Graph.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/QGSF97W4/1706.html:text/html}
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}
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@article{oh_complex_2017,
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title = {Complex Contagions with Timers},
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url = {http://arxiv.org/abs/1706.04252},
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abstract = {A great deal of effort has gone into trying to model social influence --- including the spread of behavior, norms, and ideas --- on networks. Most models of social influence tend to assume that individuals react to changes in the states of their neighbors without any time delay, but this is often not true in social contexts, where (for various reasons) different agents can have different response times. To examine such situations, we introduce the idea of a timer into threshold models of social influence. The presence of timers on nodes delays the adoption --- i.e., change of state --- of each agent, which in turn delays the adoptions of its neighbors. With a homogeneous-distributed timer, in which all nodes exhibit the same amount of delay, adoption delays are also homogeneous, so the adoption order of nodes remains the same. However, heterogeneously-distributed timers can change the adoption order of nodes and hence the "adoption paths" through which state changes spread in a network. Using a threshold model of social contagions, we illustrate that heterogeneous timers can either accelerate or decelerate the spread of adoptions compared to an analogous situation with homogeneous timers, and we investigate the relationship of such acceleration or deceleration with respect to timer distribution and network structure. We derive an analytical approximation for the temporal evolution of the fraction of adopters by modifying a pair approximation of the Watts threshold model, and we find good agreement with numerical computations. We also examine our new timer model on networks constructed from empirical data.},
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journaltitle = {{arXiv}:1706.04252 [nlin, physics:physics]},
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author = {Oh, Se-Wook and Porter, Mason A.},
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urldate = {2018-02-27},
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date = {2017-06-13},
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eprinttype = {arxiv},
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eprint = {1706.04252},
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keywords = {Mathematics - Probability, Physics - Physics and Society, Computer Science - Social and Information Networks, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Mathematics - Dynamical Systems},
|
||
file = {arXiv\:1706.04252 PDF:/home/dimitri/Zotero/storage/DC3LZPEC/Oh and Porter - 2017 - Complex Contagions with Timers.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/6FT2IFSL/1706.html:text/html}
|
||
}
|
||
|
||
@article{mellor_classifying_2018,
|
||
title = {Classifying Conversation in Digital Communication},
|
||
url = {http://arxiv.org/abs/1801.10527},
|
||
abstract = {Many studies of digital communication, in particular of Twitter, use natural language processing ({NLP}) to find topics, assess sentiment, and describe user behaviour. In finding topics often the relationships between users who participate in the topic are neglected. We propose a novel method of describing and classifying online conversations using only the structure of the underlying temporal network and not the content of individual messages. This method utilises all available information in the temporal network (no aggregation), combining both topological and temporal structure using temporal motifs and inter-event times. This allows us create an embedding of the temporal network in order to describe the behaviour of individuals and collectives over time and examine the structure of conversation over multiple timescales.},
|
||
journaltitle = {{arXiv}:1801.10527 [nlin, physics:physics]},
|
||
author = {Mellor, Andrew},
|
||
urldate = {2018-02-27},
|
||
date = {2018-01-31},
|
||
eprinttype = {arxiv},
|
||
eprint = {1801.10527},
|
||
keywords = {Physics - Physics and Society, Computer Science - Social and Information Networks, Nonlinear Sciences - Adaptation and Self-Organizing Systems},
|
||
file = {arXiv\:1801.10527 PDF:/home/dimitri/Zotero/storage/XZ25JRM6/Mellor - 2018 - Classifying Conversation in Digital Communication.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/YWMLZBQ8/1801.html:text/html}
|
||
}
|
||
|
||
@article{peel_multiscale_2017,
|
||
title = {Multiscale mixing patterns in networks},
|
||
url = {http://arxiv.org/abs/1708.01236},
|
||
abstract = {Assortative mixing in networks is the tendency for nodes with the same attributes, or metadata, to link to each other. It is a property often found in social networks manifesting as a higher tendency of links occurring between people with the same age, race, or political belief. Quantifying the level of assortativity or disassortativity (the preference of linking to nodes with different attributes) can shed light on the factors involved in the formation of links and contagion processes in complex networks. It is common practice to measure the level of assortativity according to the assortativity coefficient, or modularity in the case of discrete-valued metadata. This global value is the average level of assortativity across the network and may not be a representative statistic when mixing patterns are heterogeneous. For example, a social network spanning the globe may exhibit local differences in mixing patterns as a consequence of differences in cultural norms. Here, we introduce an approach to localise this global measure so that we can describe the assortativity, across multiple scales, at the node level. Consequently we are able to capture and qualitatively evaluate the distribution of mixing patterns in the network. We find that for many real-world networks the distribution of assortativity is skewed, overdispersed and multimodal. Our method provides a clearer lens through which we can more closely examine mixing patterns in networks.},
|
||
journaltitle = {{arXiv}:1708.01236 [physics]},
|
||
author = {Peel, Leto and Delvenne, Jean-Charles and Lambiotte, Renaud},
|
||
urldate = {2018-02-27},
|
||
date = {2017-08-03},
|
||
eprinttype = {arxiv},
|
||
eprint = {1708.01236},
|
||
keywords = {Physics - Physics and Society, Computer Science - Social and Information Networks, Physics - Data Analysis, Statistics and Probability},
|
||
file = {arXiv\:1708.01236 PDF:/home/dimitri/Zotero/storage/YDCIYN5C/Peel et al. - 2017 - Multiscale mixing patterns in networks.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/6YUS3U3T/1708.html:text/html}
|
||
}
|
||
|
||
@article{cang_evolutionary_2018,
|
||
title = {Evolutionary homology on coupled dynamical systems},
|
||
url = {http://arxiv.org/abs/1802.04677},
|
||
abstract = {Time dependence is a universal phenomenon in nature, and a variety of mathematical models in terms of dynamical systems have been developed to understand the time-dependent behavior of real-world problems. Originally constructed to analyze the topological persistence over spatial scales, persistent homology has rarely been devised for time evolution. We propose the use of a new filtration function for persistent homology which takes as input the adjacent oscillator trajectories of a dynamical system. We also regulate the dynamical system by a weighted graph Laplacian matrix derived from the network of interest, which embeds the topological connectivity of the network into the dynamical system. The resulting topological signatures, which we call evolutionary homology ({EH}) barcodes, reveal the topology-function relationship of the network and thus give rise to the quantitative analysis of nodal properties. The proposed {EH} is applied to protein residue networks for protein thermal fluctuation analysis, rendering the most accurate B-factor prediction of a set of 364 proteins. This work extends the utility of dynamical systems to the quantitative modeling and analysis of realistic physical systems.},
|
||
journaltitle = {{arXiv}:1802.04677 [math, q-bio]},
|
||
author = {Cang, Zixuan and Munch, Elizabeth and Wei, Guo-Wei},
|
||
urldate = {2018-04-05},
|
||
date = {2018-02-13},
|
||
eprinttype = {arxiv},
|
||
eprint = {1802.04677},
|
||
keywords = {Mathematics - Algebraic Topology, Mathematics - Dynamical Systems, Quantitative Biology - Quantitative Methods},
|
||
file = {arXiv\:1802.04677 PDF:/home/dimitri/Zotero/storage/4TZC2U2K/Cang et al. - 2018 - Evolutionary homology on coupled dynamical systems.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/IR4MU62L/1802.html:text/html}
|
||
}
|
||
|
||
@article{bazzi_generative_2016,
|
||
title = {Generative Benchmark Models for Mesoscale Structure in Multilayer Networks},
|
||
url = {http://arxiv.org/abs/1608.06196},
|
||
abstract = {Multilayer networks allow one to represent diverse and interdependent connectivity patterns --- e.g., time-dependence, multiple subsystems, or both --- that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate "mesoscale" (i.e., intermediate-scale) structures, such as dense sets of nodes known as "communities" that are connected sparsely to each other, to discover network features that are not apparent at the microscale or the macroscale. A variety of methods and algorithms are available to identify communities in multilayer networks, but they differ in their definitions and/or assumptions of what constitutes a community, and many scalable algorithms provide approximate solutions with little or no theoretical guarantee on the quality of their approximations. Consequently, it is crucial to develop generative models of networks to use as a common test of community-detection tools. In the present paper, we develop a family of benchmarks for detecting mesoscale structures in multilayer networks by introducing a generative model that can explicitly incorporate dependency structure between layers. Our benchmark provides a standardized set of null models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. We discuss the parameters and properties of our generative model, and we illustrate its use by comparing a variety of community-detection methods.},
|
||
journaltitle = {{arXiv}:1608.06196 [cond-mat, physics:nlin, physics:physics, stat]},
|
||
author = {Bazzi, Marya and Jeub, Lucas G. S. and Arenas, Alex and Howison, Sam D. and Porter, Mason A.},
|
||
urldate = {2018-04-30},
|
||
date = {2016-08-22},
|
||
eprinttype = {arxiv},
|
||
eprint = {1608.06196},
|
||
keywords = {Physics - Physics and Society, Statistics - Methodology, Computer Science - Social and Information Networks, Condensed Matter - Statistical Mechanics, Nonlinear Sciences - Adaptation and Self-Organizing Systems},
|
||
file = {arXiv\:1608.06196 PDF:/home/dimitri/Zotero/storage/LRM9HWTC/Bazzi et al. - 2016 - Generative Benchmark Models for Mesoscale Structur.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/JM7VWEGD/1608.html:text/html}
|
||
}
|
||
|
||
@article{sekara_fundamental_2016,
|
||
title = {Fundamental structures of dynamic social networks},
|
||
volume = {113},
|
||
rights = {© . Freely available online through the {PNAS} open access option.},
|
||
issn = {0027-8424, 1091-6490},
|
||
url = {http://www.pnas.org/content/113/36/9977},
|
||
doi = {10.1073/pnas.1602803113},
|
||
abstract = {Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.},
|
||
pages = {9977--9982},
|
||
number = {36},
|
||
journaltitle = {Proceedings of the National Academy of Sciences},
|
||
shortjournal = {{PNAS}},
|
||
author = {Sekara, Vedran and Stopczynski, Arkadiusz and Lehmann, Sune},
|
||
urldate = {2018-04-30},
|
||
date = {2016-09-06},
|
||
langid = {english},
|
||
pmid = {27555584},
|
||
keywords = {complex networks, computational social science, human dynamics, human mobility, social systems},
|
||
file = {Full Text PDF:/home/dimitri/Zotero/storage/XX3SU37E/Sekara et al. - 2016 - Fundamental structures of dynamic social networks.pdf:application/pdf;pnas.1602803113.sapp.pdf:/home/dimitri/Zotero/storage/IV3NN8R3/pnas.1602803113.sapp.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/WIREJBWU/9977.html:text/html}
|
||
}
|
||
|
||
@article{peel_detecting_2014,
|
||
title = {Detecting change points in the large-scale structure of evolving networks},
|
||
url = {http://arxiv.org/abs/1403.0989},
|
||
abstract = {Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period of time. An important task in analyzing such evolving networks is change-point detection, in which we both identify the times at which the large-scale pattern of interactions changes fundamentally and quantify how large and what kind of change occurred. Here, we formalize for the first time the network change-point detection problem within an online probabilistic learning framework and introduce a method that can reliably solve it. This method combines a generalized hierarchical random graph model with a Bayesian hypothesis test to quantitatively determine if, when, and precisely how a change point has occurred. We analyze the detectability of our method using synthetic data with known change points of different types and magnitudes, and show that this method is more accurate than several previously used alternatives. Applied to two high-resolution evolving social networks, this method identifies a sequence of change points that align with known external "shocks" to these networks.},
|
||
journaltitle = {{arXiv}:1403.0989 [physics, stat]},
|
||
author = {Peel, Leto and Clauset, Aaron},
|
||
urldate = {2018-04-30},
|
||
date = {2014-03-04},
|
||
eprinttype = {arxiv},
|
||
eprint = {1403.0989},
|
||
keywords = {Statistics - Machine Learning, Physics - Physics and Society, Computer Science - Social and Information Networks},
|
||
file = {arXiv\:1403.0989 PDF:/home/dimitri/Zotero/storage/4DBDLPT3/Peel and Clauset - 2014 - Detecting change points in the large-scale structu.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/4IGGSISH/1403.html:text/html}
|
||
}
|
||
|
||
@article{gauvin_randomized_2018,
|
||
title = {Randomized reference models for temporal networks},
|
||
url = {http://arxiv.org/abs/1806.04032},
|
||
abstract = {Many real-world dynamical systems can successfully be analyzed using the temporal network formalism. Empirical temporal networks and dynamic processes that take place in these situations show heterogeneous, non-Markovian, and intrinsically correlated dynamics, making their analysis particularly challenging. Randomized reference models ({RRMs}) for temporal networks constitute a versatile toolbox for studying such systems. Defined as ensembles of random networks with given features constrained to match those of an input (empirical) network, they may be used to identify statistically significant motifs in empirical temporal networks (i.e. overrepresented w.r.t. the null random networks) and to infer the effects of such motifs on dynamical processes unfolding in the network. However, the effects of most randomization procedures on temporal network characteristics remain poorly understood, rendering their use non-trivial and susceptible to misinterpretation. Here we propose a unified framework for classifying and understanding microcanonical {RRMs} ({MRRMs}). We use this framework to propose a canonical naming convention for existing randomization procedures, classify them, and deduce their effects on a range of important temporal network features. We furthermore show that certain classes of compatible {MRRMs} may be applied in sequential composition to generate more than a hundred new {MRRMs} from existing ones surveyed in this article. We provide a tutorial for the use of {MRRMs} to analyze an empirical temporal network and we review applications of {MRRMs} found in literature. The taxonomy of {MRRMs} we have developed provides a reference to ease the use of {MRRMs}, and the theoretical foundations laid here may further serve as a base for the development of a principled and systematic way to generate and apply randomized reference null models for the study of temporal networks.},
|
||
journaltitle = {{arXiv}:1806.04032 [physics, q-bio]},
|
||
author = {Gauvin, Laetitia and Génois, Mathieu and Karsai, Márton and Kivelä, Mikko and Takaguchi, Taro and Valdano, Eugenio and Vestergaard, Christian L.},
|
||
urldate = {2018-06-14},
|
||
date = {2018-06-11},
|
||
eprinttype = {arxiv},
|
||
eprint = {1806.04032},
|
||
keywords = {Physics - Physics and Society, Physics - Data Analysis, Statistics and Probability, Quantitative Biology - Quantitative Methods, Computer Science - Discrete Mathematics},
|
||
file = {arXiv\:1806.04032 PDF:/home/dimitri/Zotero/storage/GVBEMC2A/Gauvin et al. - 2018 - Randomized reference models for temporal networks.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/8WF5HVDE/1806.html:text/html}
|
||
}
|
||
|
||
@article{liu_eses:_2017,
|
||
title = {{ESES}: Software for Eulerian solvent excluded surface},
|
||
volume = {38},
|
||
issn = {1096-987X},
|
||
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.24682},
|
||
doi = {10.1002/jcc.24682},
|
||
shorttitle = {{ESES}},
|
||
pages = {446--466},
|
||
number = {7},
|
||
journaltitle = {Journal of Computational Chemistry},
|
||
author = {Liu, Beibei and Wang, Bao and Zhao, Rundong and Tong, Yiying and Wei, Guo-Wei},
|
||
urldate = {2018-06-18},
|
||
date = {2017-01-04},
|
||
langid = {english},
|
||
file = {Liu et al. - 2017 - ESES Software for Eulerian solvent excluded surfa.pdf:/home/dimitri/Zotero/storage/M3TJKX6T/Liu et al. - 2017 - ESES Software for Eulerian solvent excluded surfa.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/ULYNDKZZ/jcc.html:text/html}
|
||
}
|
||
|
||
@article{petri_simplicial_2018,
|
||
title = {Simplicial Activity Driven Model},
|
||
url = {http://arxiv.org/abs/1805.06740},
|
||
abstract = {Many complex systems find a convenient representation in terms of networks: structures made by pairwise interactions of elements. Their evolution is often described by temporal networks, in which links between two nodes are replaced by sequences of events describing how interactions change over time. In particular, the Activity-Driven ({AD}) model has been widely considered, as the simplicity of its definition allows for analytical insights and various refinements. For many biological and social systems however, elementary interactions involve however more than two elements, and structures such as simplicial complexes are more adequate to describe such phenomena. Here, we propose a Simplicial Activity Driven ({SAD}) model in which the building block is a simplex of nodes representing a multi-agent interaction, instead of a set of binary interactions. We compare the resulting system with {AD} models with the same numbers of events. We highlight the resulting structural differences and show analytically and numerically that the simplicial structure leads to crucial differences in the outcome of paradigmatic processes modelling disease propagation or social contagion.},
|
||
journaltitle = {{arXiv}:1805.06740 [physics]},
|
||
author = {Petri, Giovanni and Barrat, Alain},
|
||
urldate = {2018-06-18},
|
||
date = {2018-05-17},
|
||
eprinttype = {arxiv},
|
||
eprint = {1805.06740},
|
||
keywords = {Physics - Physics and Society},
|
||
file = {arXiv\:1805.06740 PDF:/home/dimitri/Zotero/storage/XJANUF3F/Petri and Barrat - 2018 - Simplicial Activity Driven Model.pdf:application/pdf;arXiv.org Snapshot:/home/dimitri/Zotero/storage/FQ3TYRYA/1805.html:text/html}
|
||
}
|
||
|
||
@article{bassett_network_2017,
|
||
title = {Network neuroscience},
|
||
volume = {20},
|
||
rights = {2017 Nature Publishing Group},
|
||
issn = {1546-1726},
|
||
url = {https://www.nature.com/articles/nn.4502},
|
||
doi = {10.1038/nn.4502},
|
||
abstract = {Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.},
|
||
pages = {353--364},
|
||
number = {3},
|
||
journaltitle = {Nature Neuroscience},
|
||
author = {Bassett, Danielle S. and Sporns, Olaf},
|
||
urldate = {2018-07-10},
|
||
date = {2017-03},
|
||
langid = {english},
|
||
file = {Bassett and Sporns - 2017 - Network neuroscience.pdf:/home/dimitri/Zotero/storage/8H5EDRXQ/Bassett and Sporns - 2017 - Network neuroscience.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/E92MLVZA/nn.html:text/html}
|
||
}
|
||
|
||
@article{newman_network_2018,
|
||
title = {Network structure from rich but noisy data},
|
||
volume = {14},
|
||
rights = {2018 The Author(s)},
|
||
issn = {1745-2481},
|
||
url = {https://www.nature.com/articles/s41567-018-0076-1},
|
||
doi = {10.1038/s41567-018-0076-1},
|
||
abstract = {A technique allows optimal inference of the structure of a network when the available observed data are rich but noisy, incomplete or otherwise unreliable.},
|
||
pages = {542--545},
|
||
number = {6},
|
||
journaltitle = {Nature Physics},
|
||
author = {Newman, M. E. J.},
|
||
urldate = {2018-07-10},
|
||
date = {2018-06},
|
||
langid = {english},
|
||
file = {Full Text PDF:/home/dimitri/Zotero/storage/F8AYYMEJ/Newman - 2018 - Network structure from rich but noisy data.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/MIZRK2YS/s41567-018-0076-1.html:text/html}
|
||
}
|
||
|
||
@article{eagle_reality_2006,
|
||
title = {Reality mining: sensing complex social systems},
|
||
volume = {10},
|
||
issn = {1617-4909, 1617-4917},
|
||
url = {https://link.springer.com/article/10.1007/s00779-005-0046-3},
|
||
doi = {10.1007/s00779-005-0046-3},
|
||
shorttitle = {Reality mining},
|
||
abstract = {We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms.},
|
||
pages = {255--268},
|
||
number = {4},
|
||
journaltitle = {Personal and Ubiquitous Computing},
|
||
shortjournal = {Pers Ubiquit Comput},
|
||
author = {Eagle, Nathan and Pentland, Alex (Sandy)},
|
||
urldate = {2018-07-23},
|
||
date = {2006-05-01},
|
||
langid = {english},
|
||
file = {Eagle and Pentland - 2006 - Reality mining sensing complex social systems.pdf:/home/dimitri/Zotero/storage/H9DUQJ6T/Eagle and Pentland - 2006 - Reality mining sensing complex social systems.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/8DH79ULJ/10.html:text/html}
|
||
}
|
||
|
||
@article{holme_temporal_2012,
|
||
title = {Temporal networks},
|
||
volume = {519},
|
||
issn = {0370-1573},
|
||
url = {http://www.sciencedirect.com/science/article/pii/S0370157312000841},
|
||
doi = {10.1016/j.physrep.2012.03.001},
|
||
series = {Temporal Networks},
|
||
abstract = {A great variety of systems in nature, society and technology–from the web of sexual contacts to the Internet, from the nervous system to power grids–can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names—temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered, but does not attempt to unify related terminology—rather, we want to make papers readable across disciplines.},
|
||
pages = {97--125},
|
||
number = {3},
|
||
journaltitle = {Physics Reports},
|
||
shortjournal = {Physics Reports},
|
||
author = {Holme, Petter and Saramäki, Jari},
|
||
urldate = {2018-07-31},
|
||
date = {2012-10-01},
|
||
file = {ScienceDirect Snapshot:/home/dimitri/Zotero/storage/KUU88J97/S0370157312000841.html:text/html}
|
||
}
|
||
|
||
@article{holme_modern_2015,
|
||
title = {Modern temporal network theory: a colloquium},
|
||
volume = {88},
|
||
issn = {1434-6028, 1434-6036},
|
||
url = {https://link.springer.com/article/10.1140/epjb/e2015-60657-4},
|
||
doi = {10.1140/epjb/e2015-60657-4},
|
||
shorttitle = {Modern temporal network theory},
|
||
abstract = {The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.},
|
||
pages = {234},
|
||
number = {9},
|
||
journaltitle = {The European Physical Journal B},
|
||
shortjournal = {Eur. Phys. J. B},
|
||
author = {Holme, Petter},
|
||
urldate = {2018-07-31},
|
||
date = {2015-09-01},
|
||
langid = {english},
|
||
file = {Snapshot:/home/dimitri/Zotero/storage/CYSLT5MA/10.html:text/html}
|
||
}
|
||
|
||
@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 = {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}
|
||
} |