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Attributed community mining using joint general non-negative matrix factorization with graph Laplacian

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  • Chen, Zigang
  • Li, Lixiang
  • Peng, Haipeng
  • Liu, Yuhong
  • Yang, Yixian

Abstract

Community mining for complex social networks with link and attribute information plays an important role according to different application needs. In this paper, based on our proposed general non-negative matrix factorization (GNMF) algorithm without dimension matching constraints in our previous work, we propose the joint GNMF with graph Laplacian (LJGNMF) to implement community mining of complex social networks with link and attribute information according to different application needs. Theoretical derivation result shows that the proposed LJGNMF is fully compatible with previous methods of integrating traditional NMF and symmetric NMF. In addition, experimental results show that the proposed LJGNMF can meet the needs of different community minings by adjusting its parameters, and the effect is better than traditional NMF in the community vertices attributes entropy.

Suggested Citation

  • Chen, Zigang & Li, Lixiang & Peng, Haipeng & Liu, Yuhong & Yang, Yixian, 2018. "Attributed community mining using joint general non-negative matrix factorization with graph Laplacian," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 324-335.
    • Handle: RePEc:eee:phsmap:v:495:y:2018:i:c:p:324-335
    DOI: 10.1016/j.physa.2017.12.038
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    References listed on IDEAS

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    1. Ma, Xiaoke & Gao, Lin & Yong, Xuerong & Fu, Lidong, 2010. "Semi-supervised clustering algorithm for community structure detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 187-197.
    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. Shang, Ronghua & Liu, Huan & Jiao, Licheng, 2017. "Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1-24.
    4. Zhang, Zhiwei & Wang, Zhenyu, 2015. "Mining overlapping and hierarchical communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 25-33.
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    Cited by:

    1. Shunli Li & Linzhang Lu & Qilong Liu & Zhen Chen, 2023. "Graph-Regularized, Sparsity-Constrained Non-Negative Matrix Factorization with Earth Mover’s Distance Metric," Mathematics, MDPI, vol. 11(8), pages 1-14, April.

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