Temporal Networks

By Petter Holme, J. Saramaki. Encyclopedia of Social Network Analysis and Mining, 2011

2,961 citations recorded by the source corpus.

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 email, 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.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/87b81de77257e61c4210cef1bf4228925fa9ab98

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Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “Temporal network theory, a subﬁeld of graph theory, can also be used to quantify how functional network properties change over time (Holme & Saramäki, 2012; W. H. Thompson, Brantefors, & Fransson, 2017; Yu et al., 2015).”
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey (2020, methodology): “networks where links do not persist for long [2], [6], [9].”
Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks (2013, background): “By this, we mean that a path ( a, b ) → ( b, c ) in a time-aggregated network does not imply that a and c are connected via a time-respecting path in the underlying temporal network [9, 15].”
The Matthew effect in empirical data (2014, background): “The Gibrat law of proportional growth [30], inspired by the assumption that the size of an enterprize and its growth are interdependent, also predates the formal introduction of the Matthew effect.”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “Time-varying networks (also named temporal networks) provide another framework for the activity-driven changing of connection topology [31, 123, 32] .”
The fundamental advantages of temporal networks (2016, methodology): “This model is designed to assess the causality between individual interactions, for example, whether the latter contacts are triggered by the former [34], or if there are strong time-dependent patterns embedded in the original data.”
Community Discovery in Dynamic Networks (2017, neutral): “Several formalisms have been proposed to represent evolving networks without loss of information: Temporal Networks (Holme and Saramäki, 2012), Time-Varying Graphs (Casteigts et al.”