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A Visual Analysis Approach for Community Detection of Multi-Context Mobile Social Networks

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Abstract

The problem of detecting community structures of a social network has been extensively studied over recent years, but most existing methods solely rely on the network structure and neglect the context information of the social relations. The main reason is that a context-rich network offers too much flexibility and complexity for automatic or manual modulation of the multifaceted context in the analysis process. We address the challenging problem of incorporating context information into the community analysis with a novel visual analysis mechanism. Our approach consists of two stages: interactive discovery of salient context, and iterative context-guided community detection. Central to the analysis process is a context relevance model (CRM) that visually characterizes the influence of a given set of contexts on the variation of the detected communities, and discloses the community structure in specific context configurations. The extracted relevance is used to drive an iterative visual reasoning process, in which the community structures are progressively discovered. We introduce a suite of visual representations to encode the community structures, the context as well as the CRM. In particular, we propose an enhanced parallel coordinates representation to depict the context and community structures, which allows for interactive data exploration and community investigation. Case studies on several datasets demonstrate the efficiency and accuracy of our approach.

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Authors and Affiliations

  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058, China

    Yu-Xin Ma, Di-Chao Peng, Wei Chen & Qun-Sheng Peng

  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310058, China

    Jia-Yi Xu & Ting Zhang

  3. Department of Mathematics, Zhejiang University, Hangzhou, 310058, China

    Cheng-Zhe Jin

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong, China

    Hua-Min Qu

Authors
  1. Yu-Xin Ma
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  2. Jia-Yi Xu
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  3. Di-Chao Peng
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  4. Ting Zhang
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  5. Cheng-Zhe Jin
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  6. Hua-Min Qu
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  7. Wei Chen
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  8. Qun-Sheng Peng
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Corresponding author

Correspondence to Wei Chen.

Additional information

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61232012, 61202279, the National High Technology Research and Development 863 Program of China under Grant No. 2012AA12090, the Natural Science Foundation of Zhejiang Province of China under Grant No. LR13F020001, and the Doctoral Fund of Ministry of Education of China under Grant No. 20120101110134.

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Ma, YX., Xu, JY., Peng, DC. et al. A Visual Analysis Approach for Community Detection of Multi-Context Mobile Social Networks. J. Comput. Sci. Technol. 28, 797–809 (2013). https://doi.org/10.1007/s11390-013-1378-5

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  • DOI: https://doi.org/10.1007/s11390-013-1378-5

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