Year: 2024
Type: preprint
Abstract: Temporal networks are commonly used to model real-life phenomena. When these phenomena represent interactions and are captured at a fine-grained temporal resolution, they are modeled as link streams. Community detection is an essential network analysis task. Although many methods exist for static networks, and some methods have been developed for temporal networks represented as sequences of snapshots, few works can handle link streams. This article introduces the first adaptation of the well-known Modularity quality function to link streams. Unlike existing methods, it is independent of the time scale of analysis. After introducing the quality function, and its relation to existing static and dynamic definitions of Modularity, we show experimentally its relevance for dynamic community evaluation.
Source: arXiv (Cornell University)
Institutions: Lyon 1 Université, Centre National de la Recherche Scientifique, Laboratoire d'Informatique en Images et Systèmes d'Information, Institut National des Sciences Appliquées de Lyon, University of Zurich, École Normale Supérieure de Lyon, Laboratoire de Physique de l'ENS de Lyon, Unité de Mathématiques Pures et Appliquées
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403362536