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Exploring triad-rich substructures by graph-theoretic characterizations in complex networks

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  • Jia, Songwei
  • Gao, Lin
  • Gao, Yong
  • Nastos, James
  • Wen, Xiao
  • Zhang, Xindong
  • Wang, Haiyang

Abstract

One of the most important problems in complex networks is how to detect communities accurately. The main challenge lies in the fact that traditional definition about communities does not always capture the intrinsic features of communities. Motivated by the observation that communities in PPI networks tend to consist of an abundance of interacting triad motifs, we define a 2-club substructure with diameter 2 possessing triad-rich property to describe a community. Based on the triad-rich substructure, we design a DIVision Algorithm using our proposed edge Niche Centrality DIVANC to detect communities effectively in complex networks. We also extend DIVANC to detect overlapping communities by proposing a simple 2-hop overlapping strategy. To verify the effectiveness of triad-rich substructures, we compare DIVANC with existing algorithms on PPI networks, LFR synthetic networks and football networks. The experimental results show that DIVANC outperforms most other algorithms significantly and, in particular, can detect sparse communities.

Suggested Citation

  • Jia, Songwei & Gao, Lin & Gao, Yong & Nastos, James & Wen, Xiao & Zhang, Xindong & Wang, Haiyang, 2017. "Exploring triad-rich substructures by graph-theoretic characterizations in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 53-69.
    • Handle: RePEc:eee:phsmap:v:468:y:2017:i:c:p:53-69
    DOI: 10.1016/j.physa.2016.10.021
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    3. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
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    Cited by:

    1. Satyaki Roy & Ahmad F. Al Musawi & Preetam Ghosh, 2023. "Inferring links in directed complex networks through feed forward loop motifs," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.

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