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Incorporating affiliation preference into overlapping community detection

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  • Feng, Liang
  • Zhao, Qianchuan
  • Zhou, Cangqi

Abstract

Community detection is an important way to understand structures of complex networks. Many conventional methods assume that each node only belongs to one community. However, nodes may have multiple memberships in real-world networks. Recently, overlapping community detection has attracted lots of attention. With the good interpretability of latent vectors, in this paper, we improve non-negative matrix factorization method by incorporating affiliation preference. Other than directly approximating original adjacent matrix of network, our proposed Bayesian Affiliation Preference based Non-negative Matrix Factorization (BAPNMF) method maximizes the likelihood of affiliation preferences for all nodes. The intuition is that nodes prefer their neighbors than non-neighbors. We define the edge preference possibility which satisfies the totality based on generative affiliation model. In the learning phase, stochastic gradient descent with bootstrap sampling is adopted. We evaluated on both synthetic and real-world networks, and results show our method outperforms state-of-art algorithms and is scalable for large-scale networks.

Suggested Citation

  • Feng, Liang & Zhao, Qianchuan & Zhou, Cangqi, 2021. "Incorporating affiliation preference into overlapping community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
  • Handle: RePEc:eee:phsmap:v:563:y:2021:i:c:s0378437120307585
    DOI: 10.1016/j.physa.2020.125429
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    References listed on IDEAS

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    1. 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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    3. Cui, Yaozu & Wang, Xingyuan & Li, Junqiu, 2014. "Detecting overlapping communities in networks using the maximal sub-graph and the clustering coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 85-91.
    4. Li, Junqiu & Wang, Xingyuan & Cui, Yaozu, 2014. "Uncovering the overlapping community structure of complex networks by maximal cliques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 398-406.
    5. Chen, Yi & Wang, Xiaolong & Xiang, Xin & Tang, Buzhou & Chen, Qingcai & Fan, Shixi & Bu, Junzhao, 2017. "Overlapping community detection in weighted networks via a Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 790-801.
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    Cited by:

    1. Shang, Ronghua & Zhang, Weitong & Zhang, Jingwen & Feng, Jie & Jiao, Licheng, 2022. "Local community detection based on higher-order structure and edge information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).

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