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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.TIME.2023.3
URN: urn:nbn:de:0030-drops-190932
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19093/
Baudin, Alexis ;
Tabourier, Lionel ;
Magnien, Clémence
LSCPM: Communities in Massive Real-World Link Streams by Clique Percolation Method
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.
BibTeX - Entry
@InProceedings{baudin_et_al:LIPIcs.TIME.2023.3,
author = {Baudin, Alexis and Tabourier, Lionel and Magnien, Cl\'{e}mence},
title = {{LSCPM: Communities in Massive Real-World Link Streams by Clique Percolation Method}},
booktitle = {30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
pages = {3:1--3:18},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-298-3},
ISSN = {1868-8969},
year = {2023},
volume = {278},
editor = {Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/19093},
URN = {urn:nbn:de:0030-drops-190932},
doi = {10.4230/LIPIcs.TIME.2023.3},
annote = {Keywords: Temporal network, Link stream, k-clique, Community detection, Clique Percolation Method, Real-world interactions}
}
