›› 2013, Vol. 28 ›› Issue (5): 797-809.doi: 10.1007/s11390-013-1378-5

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM2013 • Previous Articles     Next Articles

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

Yu-Xin Ma1 (马昱欣), Jia-Yi Xu2 (徐佳逸), Di-Chao Peng1 (彭帝超), Ting Zhang2 (张婷), Cheng-Zhe Jin3 (金呈哲), Hua-Min Qu4 (屈华民), Member, IEEE, Wei Chen1, * (陈为), Member, CCF and Qun-Sheng Peng1 (彭群生)   

  1. 1 State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China;
    2 College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China;
    3 Department of Mathematics, Zhejiang University, Hangzhou 310058, China;
    4 Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon Hong Kong, China
  • Received:2013-05-05 Revised:2013-08-02 Online:2013-09-05 Published:2013-09-05
  • Supported by:

    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.

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 flxibility 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 effciency and accuracy of our approach.

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