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Modelling of weighted evolving networks with community structures

Author

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  • Li, Chunguang
  • Chen, Guanrong

Abstract

Many social and biological networks consist of communities–groups of nodes within which links are dense but among which links are sparse. It turns out that most of these networks are best described by weighted networks, whose properties and dynamics depend not only on their structures but also on the link weights among their nodes. Recently, there are considerable interests in the study of properties as well as modelling of such networks with community structures. To our knowledge, however, no study of any weighted network model with such a community structure has been presented in the literature to date. In this paper, we propose a weighted evolving network model with a community structure. The new network model is based on the inner-community and inter-community preferential attachments and preferential strengthening mechanism. Simulation results indicate that this network model indeed reflect the intrinsic community structure, with various power-law distributions of the node degrees, link weights, and node strengths.

Suggested Citation

  • Li, Chunguang & Chen, Guanrong, 2006. "Modelling of weighted evolving networks with community structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 869-876.
    • Handle: RePEc:eee:phsmap:v:370:y:2006:i:2:p:869-876
    DOI: 10.1016/j.physa.2006.03.005
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    Cited by:

    1. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Zhi, Danyue & Song, Dongdong & Chen, Yan & de Bok, Michiel & Tavasszy, Lóránt A. & Gao, Ziyou, 2023. "Uncovering and modeling the hierarchical organization of urban heavy truck flows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    2. Sousa, R.A. & Lula-Rocha, V.N.A. & Toutain, T. & Rosário, R.S. & Cambui, E.C.B. & Miranda, J.G.V., 2020. "Preferential interaction networks: A dynamic model for brain synchronization networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    3. Sho Tsugawa & Yukihiro Matsumoto & Hiroyuki Ohsaki, 2015. "On the robustness of centrality measures against link weight quantization in social networks," Computational and Mathematical Organization Theory, Springer, vol. 21(3), pages 318-339, September.

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