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A robust multi-view clustering method for community detection combining link and content information

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Listed:
  • He, Chaobo
  • Tang, Yong
  • Liu, Hai
  • Fei, Xiang
  • Li, Hanchao
  • Liu, Shuangyin

Abstract

Community detection is an important problem of complex networks analysis and various methods have been proposed to solve it. However, most of the existing methods only use the link information. As a result, the quality of their detected communities is often poor due to the sparse and noisy data existing in link information. Actually, content information of complex networks can also help to improve the quality of community detection. In this paper, we propose a method based on Multi-View Clustering via Robust Nonnegative Matrix Factorization (MVCRNMF). This method can provide a unified framework to combine link and content information for community detection. Its key idea is to build a multi-view robust NMF model with the co-regularized constraint on community indicator matrices of link view and content view. This can make link and content information complement each other during the factorization process of NMF. We devise iterative update rules as the optimization solution to the community detection model and also give the rigorous convergence proof. It is worth noting that MVCRNMF can learn the contribution weights from link and content information adaptively and this helps to save a lot of time on tuning the weight parameters. We conduct comparative experiments on four real-world complex networks. The results demonstrate that MVCRNMF performs better than state-of-the-art methods. Additionally, results of the case study on a co-authorship network also show that MVCRNMF can obtain higher quality communities.

Suggested Citation

  • He, Chaobo & Tang, Yong & Liu, Hai & Fei, Xiang & Li, Hanchao & Liu, Shuangyin, 2019. "A robust multi-view clustering method for community detection combining link and content information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 396-411.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:396-411
    DOI: 10.1016/j.physa.2018.09.086
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    References listed on IDEAS

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    1. 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.
    2. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    3. Xiang, Ju & Hu, Tao & Zhang, Yan & Hu, Ke & Li, Jian-Ming & Xu, Xiao-Ke & Liu, Cui-Cui & Chen, Shi, 2016. "Local modularity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 451-459.
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    Cited by:

    1. Li Zhang & Ming Liu & Bo Wang & Bo Lang & Peng Yang, 2021. "Discovering communities based on mention distance," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1945-1967, March.
    2. He, Chaobo & Zhang, Qiong & Tang, Yong & Liu, Shuangyin & Zheng, Jianhua, 2019. "Community detection method based on robust semi-supervised nonnegative matrix factorization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 279-291.
    3. Yu, Hui & Chen, LuYuan & Yao, JingTao & Wang, XingNan, 2019. "A three-way clustering method based on an improved DBSCAN algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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