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LSCPM: Communities in Massive Real-World Link Streams by Clique Percolation Method

Authors Alexis Baudin, Lionel Tabourier, Clémence Magnien

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Author Details

Alexis Baudin
  • Sorbonne Université, CNRS, LIP6, F-75005 Paris, France
Lionel Tabourier
  • Sorbonne Université, CNRS, LIP6, F-75005 Paris, France
Clémence Magnien
  • Sorbonne Université, CNRS, LIP6, F-75005 Paris, France

Funding

This work is funded by the ANR (French National Agency of Research) through the ANR FiT LabCom.

Acknowledgements

The authors thank the SocioPatterns and Icon communities, for making their datasets available.

Cite AsGet BibTex

Alexis Baudin, Lionel Tabourier, and Clémence Magnien. LSCPM: Communities in Massive Real-World Link Streams by Clique Percolation Method. In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 3:1-3:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.TIME.2023.3

Abstract

Community detection is a popular approach to understand the organization of interactions in static networks. For that purpose, the Clique Percolation Method (CPM), which involves the percolation of k-cliques, is a well-studied technique that offers several advantages. Besides, studying interactions that occur over time is useful in various contexts, which can be modeled by the link stream formalism. The Dynamic Clique Percolation Method (DCPM) has been proposed for extending CPM to temporal networks. However, existing implementations are unable to handle massive datasets. We present a novel algorithm that adapts CPM to link streams, which has the advantage that it allows us to speed up the computation time with respect to the existing DCPM method. We evaluate it experimentally on real datasets and show that it scales to massive link streams. For example, it allows to obtain a complete set of communities in under twenty-five minutes for a dataset with thirty million links, what the state of the art fails to achieve even after a week of computation. We further show that our method provides communities similar to DCPM, but slightly more aggregated. We exhibit the relevance of the obtained communities in real world cases, and show that they provide information on the importance of vertices in the link streams.

Subject Classification

ACM Subject Classification
  • Theory of computation → Dynamic graph algorithms
Keywords
  • Temporal network
  • Link stream
  • k-clique
  • Community detection
  • Clique Percolation Method
  • Real-world interactions

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