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Clique-Based Method for Social Network Clustering

Author

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  • Guang Ouyang

    (Google Inc.)

  • Dipak K. Dey

    (University of Connecitcut)

  • Panpan Zhang

    (University of Connecitcut)

Abstract

In this article, we develop a clique-based method for social network clustering. We introduce a new index to evaluate the quality of clustering results, and propose an efficient algorithm based on recursive bipartition to maximize an objective function of the proposed index. The optimization problem is NP-hard, so we approximate the semi-optimal solution via an implicitly restarted Lanczos method. One of the advantages of our algorithm is that the proposed index of each community in the clustering result is guaranteed to be higher than some predetermined threshold, p, which is completely controlled by users. We also account for the situation that p is unknown. A statistical procedure of controlling both under-clustering and over-clustering errors simultaneously is carried out to select localized threshold for each subnetwork, such that the community detection accuracy is optimized. Accordingly, we propose a localized clustering algorithm based on binary tree structure. Finally, we exploit the stochastic blockmodels to conduct simulation studies and demonstrate the accuracy and efficiency of our algorithms, both numerically and graphically.

Suggested Citation

  • Guang Ouyang & Dipak K. Dey & Panpan Zhang, 2020. "Clique-Based Method for Social Network Clustering," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 254-274, April.
    • Handle: RePEc:spr:jclass:v:37:y:2020:i:1:d:10.1007_s00357-019-9310-5
    DOI: 10.1007/s00357-019-9310-5
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    References listed on IDEAS

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    1. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. Pei, Xin & Zhan, Xiu-Xiu & Jin, Zhen, 2017. "Application of pair approximation method to modeling and analysis of a marriage network," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 280-293.
    4. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
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    Cited by:

    1. Wang, Tao & Xiao, Shiying & Yan, Jun & Zhang, Panpan, 2021. "Regional and sectoral structures of the Chinese economy: A network perspective from multi-regional input–output tables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).

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