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A local perspective on community structure in multilayer networks

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  • JEUB, LUCAS G. S.
  • MAHONEY, MICHAEL W.
  • MUCHA, PETER J.
  • PORTER, MASON A.

Abstract

The analysis of multilayer networks is among the most active areas of network science, and there are several methods to detect dense “communities†of nodes in multilayer networks. One way to define a community is as a set of nodes that trap a diffusion-like dynamical process (usually a random walk) for a long time. In this view, communities are sets of nodes that create bottlenecks to the spreading of a dynamical process on a network. We analyze the local behavior of different random walks on multiplex networks (which are multilayer networks in which different layers correspond to different types of edges) and show that they have very different bottlenecks, which correspond to rather different notions of what it means for a set of nodes to be a good community. This has direct implications for the behavior of community-detection methods that are based on these random walks.

Suggested Citation

  • Jeub, Lucas G. S. & Mahoney, Michael W. & Mucha, Peter J. & Porter, Mason A., 2017. "A local perspective on community structure in multilayer networks," Network Science, Cambridge University Press, vol. 5(2), pages 144-163, June.
    • Handle: RePEc:cup:netsci:v:5:y:2017:i:02:p:144-163_00
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    Cited by:

    1. Pavel N. Krivitsky & Laura M. Koehly & Christopher Steven Marcum, 2020. "Exponential-Family Random Graph Models for Multi-Layer Networks," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 630-659, September.
    2. Ying Song & Zhiwen Zheng & Yunmei Shi & Bo Wang, 2023. "GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks," Mathematics, MDPI, vol. 11(15), pages 1-16, July.

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