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Conference papers

Detecting Stable Communities in Link Streams at Multiple Temporal Scales

Abstract : Link streams model interactions over time in a wide range of fields. Under this model, the challenge is to mine efficiently both temporal and topological structures. Community detection and change point detection are one of the most powerful tools to analyze such evolving interactions. In this paper, we build on both to detect stable community structures by identifying change points within meaningful communities. Unlike existing dynamic community detection algorithms, the proposed method is able to discover stable communities efficiently at multiple temporal scales. We test the effectiveness of our method on synthetic networks, and on high-resolution time-varying networks of contacts drawn from real social networks.
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Contributor : Remy Cazabet Connect in order to contact the contributor
Submitted on : Tuesday, June 15, 2021 - 11:20:43 PM
Last modification on : Wednesday, November 3, 2021 - 5:53:25 AM


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  • HAL Id : hal-03261697, version 1


Souâad Boudebza, Rémy Cazabet, Omar Nouali, Faiçal Azouaou. Detecting Stable Communities in Link Streams at Multiple Temporal Scales. LEG@ECML-PKDD 2019 - Third International Workshop on Advances in Managing and Mining Large Evolving Graphs, Sep 2019, Wurzburg, Germany. ⟨hal-03261697⟩



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