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Node similarity and modularity for finding communities in networks

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  • Saoud, Bilal
  • Moussaoui, Abdelouahab

Abstract

Community detection in networks has become a very important axis of research for understanding the structure of networks. Several methods have been proposed to detect the most optimal community structure in networks. In this article, we present a novel method for detecting community structure ComDBNS (Community Detection Based on Node Similarity) for unweighted and undirected networks; it performs in two steps. The first step uses the similarity between endpoints of each link to find the inter-community links to remove in order to create basic groups of nodes properly connected. In the second step we propose a strategy to merge these initial groups to identify the final community structure (with k communities or the structure that maximizes the modularity in Community Detection Based on Node Similarity and Modularity Q (ComDBNSQ)). The proposed method is tested on the real and computer-generated networks, and it demonstrates the effectiveness and correctness of the method. Also, the method saves the time complexity.

Suggested Citation

  • Saoud, Bilal & Moussaoui, Abdelouahab, 2018. "Node similarity and modularity for finding communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1958-1966.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1958-1966
    DOI: 10.1016/j.physa.2017.11.110
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    Cited by:

    1. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    2. Hesamipour, Sajjad & Balafar, Mohammad Ali, 2019. "A new method for detecting communities and their centers using the Adamic/Adar Index and game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    3. Wu, Liuyi & Dong, Lijun & Wang, Yi & Zhang, Feng & Lee, Victor E. & Kang, Xiaojun & Liang, Qingzhong, 2018. "Uniform-scale assessment of role minimization in bipartite networks and its application to access control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 381-397.

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