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Longitudinal modularity, a modularity for link streams

delete2025-02-12
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OA
AI
V
Victor Brabant *
Y
Yasaman Asgari
P
Pierre Borgnat
A
Angela Bonifati
R
Rémy Cazabet
DOI:10.1140/epjds/s13688-025-00529-xdelete
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Abstract

Abstract

En 中文
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 directly 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.
Keywords:
Temporal networks
Community structures
Modularity
Link streams

Journal

EPJ Data Science cover
EPJ Data Science
IF:
2.5
Papers:
680
Citations:
1.6K

Organization

U
Universite Claude Bernard Lyon 1
Scholars:
2.4W
Papers: 1.7W
Citations: 156
C
centre national de la recherche scientifique (cnrs)
Scholars:
24.5W
Papers: 18.2W
Citations: 279
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