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Consensus-based methodology for detection communities in multilayered networks

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  • Karimi-Majd, Amir-Mohsen
  • Fathian, Mohammad
  • Makrehchi, Masoud

Abstract

Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users’ behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:494:y:2018:i:c:p:547-558
    DOI: 10.1016/j.physa.2017.11.130
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    References listed on IDEAS

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    1. 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.
    2. Ma, Yang & Cheng, Guangquan & Liu, Zhong & Xie, Fuli, 2017. "Fuzzy nodes recognition based on spectral clustering in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 792-797.
    3. Franke, R., 2016. "CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 384-408.
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