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<p>We propose an extension of a recently introduced algorithm for detecting overlapping communities in bipartite networks based on the maximization of the Fuzzy Entropic Barber Modularity. Similar to Fuzzy clustering, nodes are given a membership distribution which represents how much a node fits into each of the communities. Together with the fuzzyfied version of the Barber modularity, in a linear combination the Fuzzy Entropic Barber Modularity optimizes an entropic term which accounts for the uncertainty of the bipartite network itself, and also a new term here introduced to control the number of communities. The corresponding temperature and resolution parameters allow for an online optimization of the number of communities and fuzzy memberships.</p>

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An algorithm with tunable resolution for detecting overlapping communities in bipartite networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月30日

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  • Lorenzo Federico,
  • Andrea Auconi,
  • Giulia Anna Bianca Bordi,
  • Simone Piazza,
  • Guido Caldarelli

摘要

We propose an extension of a recently introduced algorithm for detecting overlapping communities in bipartite networks based on the maximization of the Fuzzy Entropic Barber Modularity. Similar to Fuzzy clustering, nodes are given a membership distribution which represents how much a node fits into each of the communities. Together with the fuzzyfied version of the Barber modularity, in a linear combination the Fuzzy Entropic Barber Modularity optimizes an entropic term which accounts for the uncertainty of the bipartite network itself, and also a new term here introduced to control the number of communities. The corresponding temperature and resolution parameters allow for an online optimization of the number of communities and fuzzy memberships.