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A layer reduction based community detection algorithm on multiplex networks

Author

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  • Wang, Xiaodong
  • Liu, Jing

Abstract

Detecting hidden communities is important for the analysis of complex networks. However, many algorithms have been designed for single layer networks (SLNs) while just a few approaches have been designed for multiplex networks (MNs). In this paper, we propose an algorithm based on layer reduction for detecting communities on MNs, which is termed as LRCD-MNs. First, we improve a layer reduction algorithm termed as neighaggre to combine similar layers and keep others separated. Then, we use neighaggre to find the community structure hidden in MNs. Experiments on real-life networks show that neighaggre can obtain higher relative entropy than the other algorithm. Moreover, we apply LRCD-MNs on some real-life and synthetic multiplex networks and the results demonstrate that, although LRCD-MNs does not have the advantage in terms of modularity, it can obtain higher values of surprise, which is used to evaluate the quality of partitions of a network.

Suggested Citation

  • Wang, Xiaodong & Liu, Jing, 2017. "A layer reduction based community detection algorithm on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 244-252.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:244-252
    DOI: 10.1016/j.physa.2016.11.036
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    Cited by:

    1. Karimi-Majd, Amir-Mohsen & Fathian, Mohammad & Makrehchi, Masoud, 2018. "Consensus-based methodology for detection communities in multilayered networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 547-558.
    2. Li, Liqiang & Liu, Jing, 2020. "The aggregation of multiplex networks based on the similarity of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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