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A framework of community detection based on individual labels in attribute networks

Author

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  • Nan, Dong-Yang
  • Yu, Wei
  • Liu, Xiao
  • Zhang, Yun-Peng
  • Dai, Wei-Di

Abstract

Community detection is an important problem for understanding the structure and function of complex networks and has attracted a lot of attention in recent decades. Most community detection algorithms only focus on the topology of networks. However, there is still much valuable information hidden in the networks, such as the attributes or content of the nodes and the useful prior information. Obviously, taking full advantage of these resources can improve the effectiveness of community detection. In this paper, we present a semi-supervised community detection framework named SCDAN (Semi-supervised Community Detection in Attribute Networks), in which a non-negative matrix factorization model is utilized to effectively integrate network topology, node attributes and individual labels simultaneously. The comparative experiments on real-world networks show that SCDAN significantly improves the performance of community detection and provides semantic interpretation of communities.

Suggested Citation

  • Nan, Dong-Yang & Yu, Wei & Liu, Xiao & Zhang, Yun-Peng & Dai, Wei-Di, 2018. "A framework of community detection based on individual labels in attribute networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 523-536.
    • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:523-536
    DOI: 10.1016/j.physa.2018.08.100
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    Cited by:

    1. Zihe Zhou & Bo Tian, 2019. "Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach," Future Internet, MDPI, vol. 11(12), pages 1-16, December.

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