
@article{tabourier_predicting_2016,
	title = {Predicting links in ego-networks using temporal information},
	volume = {5},
	rights = {2016 Tabourier et al.},
	issn = {2193-1127},
	url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0062-0},
	doi = {10.1140/epjds/s13688-015-0062-0},
	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.},
	pages = {1},
	number = {1},
	journaltitle = {{EPJ} Data Science},
	author = {Tabourier, Lionel and Libert, Anne-Sophie and Lambiotte, Renaud},
	urldate = {2018-02-13},
	date = {2016-12},
	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}
}

@article{kivela_multilayer_2014,
	title = {Multilayer Networks},
	volume = {2},
	issn = {2051-1310, 2051-1329},
	url = {http://arxiv.org/abs/1309.7233},
	doi = {10.1093/comnet/cnu016},
	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.},
	pages = {203--271},
	number = {3},
	journaltitle = {Journal of Complex Networks},
	author = {Kivelä, Mikko and Arenas, Alexandre and Barthelemy, Marc and Gleeson, James P. and Moreno, Yamir and Porter, Mason A.},
	urldate = {2018-02-13},
	date = {2014-09-01},
	eprinttype = {arxiv},
	eprint = {1309.7233},
	keywords = {Physics - Physics and Society, Computer Science - Social and Information Networks},
	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}
}

@article{porter_dynamical_2014,
	title = {Dynamical Systems on Networks: A Tutorial},
	url = {http://arxiv.org/abs/1403.7663},
	shorttitle = {Dynamical Systems on Networks},
	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.},
	journaltitle = {{arXiv}:1403.7663 [cond-mat, physics:nlin, physics:physics]},
	author = {Porter, Mason A. and Gleeson, James P.},
	urldate = {2018-02-13},
	date = {2014-03-29},
	eprinttype = {arxiv},
	eprint = {1403.7663},
	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},
	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}
}

@article{casteigts_time-varying_2012,
	title = {Time-varying graphs and dynamic networks},
	volume = {27},
	issn = {1744-5760},
	url = {https://doi.org/10.1080/17445760.2012.668546},
	doi = {10.1080/17445760.2012.668546},
	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}.},
	pages = {387--408},
	number = {5},
	journaltitle = {International Journal of Parallel, Emergent and Distributed Systems},
	author = {Casteigts, Arnaud and Flocchini, Paola and Quattrociocchi, Walter and Santoro, Nicola},
	urldate = {2018-02-21},
	date = {2012-10-01},
	keywords = {social networks, delay-tolerant networks, distributed computing, dynamic graphs, opportunistic networks, time-varying graphs},
	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}
}

@article{kuhn_dynamic_2011,
	title = {Dynamic Networks: Models and Algorithms},
	volume = {42},
	issn = {0163-5700},
	url = {http://doi.acm.org/10.1145/1959045.1959064},
	doi = {10.1145/1959045.1959064},
	shorttitle = {Dynamic Networks},
	pages = {82--96},
	number = {1},
	journaltitle = {{SIGACT} News},
	author = {Kuhn, Fabian and Oshman, Rotem},
	urldate = {2018-02-21},
	date = {2011-03},
	file = {kuhn2011.pdf:/home/dimitri/Zotero/storage/WEN85Y2C/kuhn2011.pdf:application/pdf}
}

@article{michail_introduction_2016,
	title = {An Introduction to Temporal Graphs: An Algorithmic Perspective},
	volume = {12},
	issn = {1542-7951},
	url = {https://doi.org/10.1080/15427951.2016.1177801},
	doi = {10.1080/15427951.2016.1177801},
	shorttitle = {An Introduction to Temporal Graphs},
	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.},
	pages = {239--280},
	number = {4},
	journaltitle = {Internet Mathematics},
	author = {Michail, Othon},
	urldate = {2018-02-21},
	date = {2016-07-03},
	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}
}

@article{kempe_connectivity_2002,
	title = {Connectivity and Inference Problems for Temporal Networks},
	volume = {64},
	issn = {0022-0000},
	url = {http://www.sciencedirect.com/science/article/pii/S0022000002918295},
	doi = {10.1006/jcss.2002.1829},
	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.},
	pages = {820--842},
	number = {4},
	journaltitle = {Journal of Computer and System Sciences},
	shortjournal = {Journal of Computer and System Sciences},
	author = {Kempe, David and Kleinberg, Jon and Kumar, Amit},
	urldate = {2018-02-22},
	date = {2002-06-01},
	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}
}

@inproceedings{mertzios_temporal_2013,
	title = {Temporal Network Optimization Subject to Connectivity Constraints},
	isbn = {978-3-642-39211-5 978-3-642-39212-2},
	url = {https://link.springer.com/chapter/10.1007/978-3-642-39212-2_57},
	doi = {10.1007/978-3-642-39212-2_57},
	series = {Lecture Notes in Computer Science},
	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.},
	eventtitle = {International Colloquium on Automata, Languages, and Programming},
	pages = {657--668},
	booktitle = {Automata, Languages, and Programming},
	publisher = {Springer, Berlin, Heidelberg},
	author = {Mertzios, George B. and Michail, Othon and Chatzigiannakis, Ioannis and Spirakis, Paul G.},
	urldate = {2018-02-22},
	date = {2013-07-08},
	langid = {english},
	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}
}

@article{akrida_ephemeral_2016,
	title = {Ephemeral networks with random availability of links: The case of fast networks},
	volume = {87},
	issn = {0743-7315},
	url = {http://www.sciencedirect.com/science/article/pii/S0743731515001872},
	doi = {10.1016/j.jpdc.2015.10.002},
	shorttitle = {Ephemeral networks with random availability of links},
	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.},
	pages = {109--120},
	journaltitle = {Journal of Parallel and Distributed Computing},
	shortjournal = {Journal of Parallel and Distributed Computing},
	author = {Akrida, Eleni C. and Gąsieniec, Leszek and Mertzios, George B. and Spirakis, Paul G.},
	urldate = {2018-02-22},
	date = {2016-01-01},
	keywords = {Availability, Diameter, Random input, Temporal networks},
	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}
}

@article{benson_simplicial_2018,
	title = {Simplicial Closure and Higher-order Link Prediction},
	url = {http://arxiv.org/abs/1802.06916},
	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.},
	journaltitle = {{arXiv}:1802.06916 [cond-mat, physics:physics, stat]},
	author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon},
	urldate = {2018-02-27},
	date = {2018-02-19},
	eprinttype = {arxiv},
	eprint = {1802.06916},
	keywords = {Statistics - Machine Learning, Mathematics - Algebraic Topology, Physics - Physics and Society, Computer Science - Social and Information Networks, Condensed Matter - Statistical Mechanics},
	file = {arXiv\:1802.06916 PDF:/home/dimitri/Zotero/storage/C5IG7QGL/Benson et al. - 2018 - Simplicial Closure and Higher-order Link Predictio.pdf:application/pdf}
}

@article{mellor_temporal_2017,
	title = {The Temporal Event Graph},
	issn = {2051-1310, 2051-1329},
	url = {http://arxiv.org/abs/1706.02128},
	doi = {10.1093/comnet/cnx048},
	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.},
	journaltitle = {Journal of Complex Networks},
	author = {Mellor, Andrew},
	urldate = {2018-02-27},
	date = {2017-10-06},
	eprinttype = {arxiv},
	eprint = {1706.02128},
	keywords = {Physics - Physics and Society, Computer Science - Social and Information Networks, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Physics - Data Analysis, Statistics and Probability},
	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}
}

@article{oh_complex_2017,
	title = {Complex Contagions with Timers},
	url = {http://arxiv.org/abs/1706.04252},
	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.},
	journaltitle = {{arXiv}:1706.04252 [nlin, physics:physics]},
	author = {Oh, Se-Wook and Porter, Mason A.},
	urldate = {2018-02-27},
	date = {2017-06-13},
	eprinttype = {arxiv},
	eprint = {1706.04252},
	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}
}