Document Open Access Logo

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

Authors Alexis Baudin, Lionel Tabourier, Clémence Magnien

PDF
Thumbnail PDF

File

LIPIcs.TIME.2023.3.pdf
  • Filesize: 1.59 MB
  • 18 pages

Document Identifiers

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 As Get 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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Supplementary Materials

References

  1. Thomas Aynaud and Jean-Loup Guillaume. Static community detection algorithms for evolving networks. In 8th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks, pages 513-519. IEEE, 2010. Google Scholar
  2. Alexis Baudin, Maximilien Danisch, Sergey Kirgizov, Clémence Magnien, and Marwan Ghanem. Clique percolation method: memory efficient almost exact communities. In Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings, Part II, pages 113-127. Springer, 2022. Google Scholar
  3. Alexis Baudin, Clémence Magnien, and Lionel Tabourier. Faster maximal clique enumeration in large real-world link streams. arXiv preprint arXiv:2302.00360, 2023. Google Scholar
  4. Souâad Boudebza, Rémy Cazabet, Faiçal Azouaou, and Omar Nouali. OLCPM: An online framework for detecting overlapping communities in dynamic social networks. Computer Communications, 123:36-51, 2018. Code available at: URL: https://figshare.com/articles/software/OLCPM_algorithm/5914846.
  5. Arnaud Casteigts, Paola Flocchini, Walter Quattrociocchi, and Nicola Santoro. Time-varying graphs and dynamic networks. International Journal of Parallel, Emergent and Distributed Systems, 27(5):387-408, 2012. Google Scholar
  6. Rémy Cazabet, Giulio Rossetti, and Fredéric Amblard. Dynamic community detection, 2017. Google Scholar
  7. Wanyun Cui, Yanghua Xiao, Haixun Wang, Yiqi Lu, and Wei Wang. Online search of overlapping communities. In Proceedings of the 2013 ACM SIGMOD international conference on Management of data, pages 277-288, 2013. Google Scholar
  8. Maximilien Danisch, Oana Balalau, and Mauro Sozio. Listing k-cliques in sparse real-world graphs. In Proceedings of the 2018 World Wide Web Conference, pages 589-598, 2018. Google Scholar
  9. Santo Fortunato. Community detection in graphs. Physics reports, 486(3-5):75-174, 2010. Google Scholar
  10. Julie Fournet and Alain Barrat. Contact patterns among high school students. PloS one, 9(9):e107878, 2014. Data available at: URL: http://www.sociopatterns.org/datasets/high-school-dynamic-contact-networks.
  11. Petter Holme and Jari Saramäki. Temporal networks. Physics reports, 519(3):97-125, 2012. Google Scholar
  12. Lorenzo Isella, Juliette Stehlé, Alain Barrat, Ciro Cattuto, Jean-François Pinton, and Wouter Van den Broeck. What’s in a crowd? analysis of face-to-face behavioral networks. Journal of Theoretical Biology, 271(1):166-180, 2011. Data available at: URL: http://www.sociopatterns.org/datasets/infectious-sociopatterns/.
  13. Moses C Kiti, Michele Tizzoni, Timothy M Kinyanjui, Dorothy C Koech, Patrick K Munywoki, Milosch Meriac, Luca Cappa, André Panisson, Alain Barrat, Ciro Cattuto, et al. Quantifying social contacts in a household setting of rural kenya using wearable proximity sensors. EPJ data science, 5:1-21, 2016. Google Scholar
  14. Jussi M Kumpula, Mikko Kivelä, Kimmo Kaski, and Jari Saramäki. Sequential algorithm for fast clique percolation. Physical review E, 78(2):026109, 2008. Google Scholar
  15. Matthieu Latapy, Tiphaine Viard, and Clémence Magnien. Stream graphs and link streams for the modeling of interactions over time. Social Network Analysis and Mining, 8:1-29, 2018. Google Scholar
  16. Matteo Magnani, Obaida Hanteer, Roberto Interdonato, Luca Rossi, and Andrea Tagarelli. Community detection in multiplex networks. ACM Computing Surveys (CSUR), 54(3):1-35, 2021. Google Scholar
  17. Alan Mislove. Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems. PhD thesis, Rice University, Department of Computer Science, May 2009. Data available at: URL: https://socialnetworks.mpi-sws.org/data-wosn2008.html.
  18. Nam P Nguyen, Thang N Dinh, Sindhura Tokala, and My T Thai. Overlapping communities in dynamic networks: their detection and mobile applications. In Proceedings of the 17th annual international conference on Mobile computing and networking, pages 85-96, 2011. Google Scholar
  19. Gergely Palla, Albert-László Barabási, and Tamás Vicsek. Quantifying social group evolution. Nature, 446(7136):664-667, 2007. Google Scholar
  20. Gergely Palla, Imre Derényi, Illés Farkas, and Tamás Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. nature, 435(7043):814-818, 2005. Google Scholar
  21. Gang Pan, Wangsheng Zhang, Zhaohui Wu, and Shijian Li. Online community detection for large complex networks. PloS one, 9(7):e102799, 2014. Google Scholar
  22. Fergal Reid, Aaron McDaid, and Neil Hurley. Percolation computation in complex networks. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 274-281. IEEE, 2012. Google Scholar
  23. Giulio Rossetti, Luca Pappalardo, Dino Pedreschi, and Fosca Giannotti. Tiles: an online algorithm for community discovery in dynamic social networks. Machine Learning, 106:1213-1241, 2017. Google Scholar
  24. Michael T Schaub, Jean-Charles Delvenne, Martin Rosvall, and Renaud Lambiotte. The many facets of community detection in complex networks. Applied network science, 2(1):1-13, 2017. Google Scholar
  25. Yifu Tang, Jianxin Li, Nur Al Hasan Haldar, Ziyu Guan, Jiajie Xu, and Chengfei Liu. Reliable community search in dynamic networks. arXiv preprint arXiv:2202.01525, 2022. Google Scholar
  26. Robert E. Tarjan and Jan van Leeuwen. Worst-case analysis of set union algorithms. J. ACM, 31(2):245-281, March 1984. Google Scholar
  27. Tiphaine Viard, Matthieu Latapy, and Clémence Magnien. Computing maximal cliques in link streams. Theoretical Computer Science, 609:245-252, 2016. Google Scholar
  28. Tiphaine Viard, Clémence Magnien, and Matthieu Latapy. Enumerating maximal cliques in link streams with durations. Information Processing Letters, 133:44-48, 2018. Google Scholar
  29. Dingqi Yang, Daqing Zhang, Vincent W Zheng, and Zhiyong Yu. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1):129-142, 2014. Data available at: URL: https://sites.google.com/site/yangdingqi/home/foursquare-dataset.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact