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Fuzzy overlapping community detection based on local random walk and multidimensional scaling

Author

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  • Wang, Wenjun
  • Liu, Dong
  • Liu, Xiao
  • Pan, Lin

Abstract

A fuzzy overlapping community is an important kind of overlapping community in which each node belongs to each community to different extents. It exists in many real networks but how to identify a fuzzy overlapping community is still a challenging task. In this work, the concept of local random walk and a new distance metric are introduced. Based on the new distance measurement, the dissimilarity index between each node of a network is calculated firstly. Then in order to keep the original node distance as much as possible, the network structure is mapped into low-dimensional space by the multidimensional scaling (MDS). Finally, the fuzzy c-means clustering is employed to find fuzzy communities in a network. The experimental results show that the proposed algorithm is effective and efficient to identify the fuzzy overlapping communities in both artificial networks and real-world networks.

Suggested Citation

  • Wang, Wenjun & Liu, Dong & Liu, Xiao & Pan, Lin, 2013. "Fuzzy overlapping community detection based on local random walk and multidimensional scaling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6578-6586.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:24:p:6578-6586
    DOI: 10.1016/j.physa.2013.08.028
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhou, Xu & Liu, Yanheng & Zhang, Jindong & Liu, Tuming & Zhang, Di, 2015. "An ant colony based algorithm for overlapping community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 289-301.
    2. You, Tao & Cheng, Hui-Min & Ning, Yi-Zi & Shia, Ben-Chang & Zhang, Zhong-Yuan, 2016. "Community detection in complex networks using density-based clustering algorithm and manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 221-230.
    3. Gholami, Maryam & Sheikhahmadi, Amir & Khamforoosh, Keyhan & Jalili, Mahdi, 2022. "Overlapping community detection in networks based on Neutrosophic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    4. Yang, Jin-Xuan & Zhang, Xiao-Dong, 2017. "Finding overlapping communities using seed set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 96-106.
    5. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
    6. Li, Yafang & Jia, Caiyan & Li, Jianqiang & Wang, Xiaoyang & Yu, Jian, 2018. "Enhanced semi-supervised community detection with active node and link selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 219-232.
    7. Liu, Dong & Liu, Xiao & Wang, Wenjun & Bai, Hongyu, 2014. "Semi-supervised community detection based on discrete potential theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 173-182.

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