6. Community detection in temporal networks

By Konstantin Avrachenkov, INRIA Sophia-Antipolis, France, k.avrachenkov@inria.fr | Maximilien Dreveton, Inria Sophia-Antipolis, France, maximilien.dreveton@gmail.com

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Published: 06 Oct 2022

© 2022 Konstantin Avrachenkov | Maximilien Dreveton

Abstract

Previous chapters focus on the study of static interactions, represented by a binary number or a positive weight. Nevertheless, in many application domains, interactions vary over time. The longitudinal nature of such datasets calls for replacing classical graph-based models with temporal network models represented by tensors (Holme and Saramäki, 2012; Kivelä et al., 2014). We note that taking into account the temporal aspects not carefully, e.g., by aggregating or smoothing the temporal data along the time axis, can lead to a loss of valuable information.