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Community detection based on human social behavior

Author

Listed:
  • Sheng, Jinfang
  • Hu, Jie
  • Sun, Zejun
  • Wang, Bin
  • Ullah, Aman
  • Wang, Kai
  • Zhang, Junkai

Abstract

Community structure is widespread in the form of complex networks and is important. However, research on detecting community structure (community detection) still has some unsolved problems. In this paper, we introduce a new community detection algorithm, called Commansor (Community Detection Based on HumanSocial Behavior), which automatically detects communities in the network by simulating human social behavior. The fundamental principle of Commansor is finding the social comfort zone of human beings in the social process and simulating the evolution of social network structure in the information spreading process. Different closeness relationships between nodes form a comfort zone for each node, and the process of information spreading has an important impact on the evolution of the community structure. The main steps of the Commansor algorithm include (a) finding a comfort zone for each node by using closeness matrices, (b) randomly selecting an associated node from the comfort zone for each node to reconstruct a simplified network, and (c) adjusting the simplified network structure by simulating the process of information spreading in human society. We compared Commansor with a range of typical algorithms by performing extensive experiments on synthetic networks and real-world networks. The experiments proved that, in most cases, our algorithm is superior to the compared algorithms in terms of the quality of community detection.

Suggested Citation

  • Sheng, Jinfang & Hu, Jie & Sun, Zejun & Wang, Bin & Ullah, Aman & Wang, Kai & Zhang, Junkai, 2019. "Community detection based on human social behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119310246
    DOI: 10.1016/j.physa.2019.121765
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    Cited by:

    1. Leong, Rachel A.T. & Fung, Tze Kwan & Sachidhanandam, Uma & Drillet, Zuzana & Edwards, Peter J. & Richards, Daniel R., 2020. "Use of structural equation modeling to explore influences on perceptions of ecosystem services and disservices attributed to birds in Singapore," Ecosystem Services, Elsevier, vol. 46(C).
    2. Fligg, Robert A. & Ballantyne, Brian & Robinson, Derek T., 2022. "Informality within Indigenous land management: A land-use study at Curve Lake First Nation, Canada," Land Use Policy, Elsevier, vol. 112(C).
    3. Mohd Azam, Siti Balqis & Abu Bakar, Siti Hajar & Mohd Yusoff, Jal Zabdi & Abdul Rauf, Siti Hajar, 2021. "A case study on academic and vocational training for child offenders undergoing a multisystemic therapy-based rehabilitation order in Malaysia," Children and Youth Services Review, Elsevier, vol. 122(C).
    4. Gotham, Dzintars & Moja, Lorenzo & van der Heijden, Maarten & Paulin, Sarah & Smith, Ingrid & Beyer, Peter, 2021. "Reimbursement models to tackle market failures for antimicrobials: Approaches taken in France, Germany, Sweden, the United Kingdom, and the United States," Health Policy, Elsevier, vol. 125(3), pages 296-306.

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