https://doi.org/10.1140/epjds/s13688-025-00529-x
Research
Longitudinal modularity, a modularity for link streams
1
UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622, Villeurbanne, France
2
Department of Mathematical Modeling and Machine Learning, Digital Society Initiative (DSI), University of Zurich, CH-8057, Zurich, Switzerland
3
CNRS, Univ de Lyon, ENSL, Laboratoire de Physique, F-69342, Lyon, France
Received:
10
September
2024
Accepted:
27
January
2025
Published online:
12
February
2025
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.
Key words: Temporal networks / Community structures / Modularity / Link streams
© The Author(s) 2025
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