
TL;DR
Temporal networks extend traditional static graph models by incorporating the timing of interactions, which significantly influences the dynamics of processes like disease spread and information flow.
Contribution
This paper introduces the emerging field of temporal networks, discussing analysis methods and models that account for the timing of interactions, advancing understanding beyond static network theory.
Findings
Temporal structure affects dynamical processes on networks.
Methods for analyzing temporal and topological features are discussed.
Temporal networks require different analytical approaches than static 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 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…
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