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Overlapping community detection in networks based on link partitioning and partitioning around medoids

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  • Alexander Ponomarenko
  • Leonidas Pitsoulis
  • Marat Shamshetdinov

Abstract

In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.

Suggested Citation

  • Alexander Ponomarenko & Leonidas Pitsoulis & Marat Shamshetdinov, 2021. "Overlapping community detection in networks based on link partitioning and partitioning around medoids," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-43, August.
    • Handle: RePEc:plo:pone00:0255717
    DOI: 10.1371/journal.pone.0255717
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    References listed on IDEAS

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    1. T. S. Evans & R. Lambiotte, 2010. "Line graphs of weighted networks for overlapping communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(2), pages 265-272, September.
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    Cited by:

    1. Tyler J. Gandee & Sean C. Glaze & Philippe J. Giabbanelli, 2024. "A Visual Analytics Environment for Navigating Large Conceptual Models by Leveraging Generative Artificial Intelligence," Mathematics, MDPI, vol. 12(13), pages 1-23, June.

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