›› 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.

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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.

[1] Cho E, Myers S A, Leskovec J. Friendship and mobility: User movement in location-based social networks. In Proc. the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2011, pp.1082-1090.

[2] Scott J, Gass R, Crowcroft J et al. CRAWDAD metadata: Cambridge/haggle/imote/infocom2006 (v. 2009-05-29). http://tinyurl.com/6ugh4nz, August 2013.

[3] Kernighan B W, Lin S. An effcient heuristic procedure for partitioning graphs. Bell System Technical Journal, 1970, 49(2): 291-307.

[4] Girvan M, Newman ME J. Community structure in social and biological networks. Proc. the National Academy of Sciences of the United States of America, 2002, 99(12): 7821-7826.

[5] Newman M E J. Fast algorithm for detecting community structure in networks. Physical Review E, 2004, 69(6): 066133.

[6] Traag V A, Bruggeman J. Community detection in networks with positive and negative links. Physical Review E, 2009, 80(3): 036115.

[7] Tantipathananandh C, Berger-Wolf T, Kempe D. A framework for community identification in dynamic social networks. In Proc. the 13th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2007, pp.717-726.

[8] Eagle N, Pentland A S, Lazer D. Inferring friendship network structure by using mobile phone data. Proc. the National Academy of Sciences of the United States of America, 2009,106(36): 15274-15278.

[9] Blondel V D, Guillaume J L, Lambiotte R et al. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10): P10008.

[10] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition). Springer, 2009.

[11] Donetti L, Munoz M A. Detecting network communities: A new systematic and effcient algorithm. Journal of Statistical Mechanics: Theory and Experiment, 2004, 2004: P10012.

[12] Fortunato S. Community detection in graphs. Physics Reports, 2010, 486(3/5): 75-174.

[13] Chen J C, Yuan B. Detecting functional modules in the yeast protein{protein interaction network. Bioinformatics, 2006, 22(18): 2283-2290.

[14] Newman M E J, Girvan M. Finding and evaluating community structure in networks. Physical Review E, 2004, 69(2): 026113.

[15] Mitrovisc M, Tadisac B. Spectral and dynamical properties in classes of sparse networks with mesoscopic inhomogeneities. Physical Review E, 2009, 80(2): 026123.

[16] Garrido P L, Marro J, Munoz M A (eds.). Modeling Cooperative Behavior in the Social Sciences: Eighth Granada Lectures on Modeling Cooperative Behavior in the Social. American Institute of Physics, 2005.

[17] Simonsen I. Diffusion and networks: A powerful combination! Physica A: Statistical Mechanics and Its Applications, 2005, 357(2): 317-330.

[18] Backstrom L, Leskovec J. Supervised random walks: Predicting and recommending links in social networks. In Proc. the 4th ACM International Conference on Web Search and Data Mining, February 2011, pp.635-644.

[19] Zhou H. Distance, dissimilarity index, and network community structure. Physical Review E, 2003, 67(6): 061901.

[20] Ronhovde P, Nussinov Z. Local resolution-limit-free potts model for community detection. Physical Review E, 2010, 81(4): 046114.

[21] Lambiotte R, Delvenne J C, Barahona M. Laplacian dynamics and multiscale modular structure in networks. ArXiv e-prints arXiv:0812.1770, http://arxiv.org/abs/0812.1770, May 2013.

[22] Palla G, Deréenyi I, Farkas I, Vicsek T. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 2005, 435: 814-818.

[23] Gong M G, Zhang L J, Ma J J, Jiao L C. Community detection in dynamic social networks based on multiobjective immune algorithm. Journal of Computer Science and Technology, 2012, 27(3): 455-467.

[24] Chan S Y, Hui P, Xu K. Community detection of time-varying mobile social networks. In Proc. the 1st Int. Conf. Complex Science, Feb. 2009, pp.1154-1159.

[25] Wu Z H, Lin Y F, Gregory S, Wan H Y, Tian S F. Balanced multi-label propagation for overlapping community detection in social networks. Journal of Computer Science and Technology, 2012, 27(3): 468-479.

[26] Wan H Y, Lin Y F, Wu Z H, Huang H K. Discovering typed communities in mobile social networks. Journal of Computer Science and Technology, 2012, 27(3): 480-491.

[27] Wang X, Zhou X, Wang S. Summarizing large-scale database schema using community detection. Journal of Computer Science and Technology, 2012, 27(3): 515-526.

[28] Batagelj V, Mrvar A. Pajek-program for large network analysis. Connections, 1998, 21(2): 47-57.

[29] Heer J, Boyd D. Vizster: Visualizing online social networks. In Proc. IEEE Symposium on Information Visualization, October 2005, pp.32-39.

[30] Smith M A, Shneiderman B, Milic-Frayling N et al. Analyzing (social media) networks with NodeXL. In Proc. the 4th International Conference on Communities and Technologies, June 2009, pp.255-264.

[31] Bastian M, Heymann S, Jacomy M. Gephi: An open source software for exploring and manipulating networks. In Proc. International AAAI Conference on Weblogs and Social Media, May 2009, http://www.aaai.org/ocs/index.php/ ICWSM/09/paper/view/154/1009, Aug. 2013.

[32] Hu Y. Effcient, high-quality force-directed graph drawing. Mathematica Journal, 2005, 10(1): 37-71.

[33] Holten D. Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 741-748.

[34] Wong N, Carpendale S, Greenberg S. Edgelens: An interactive method for managing edge congestion in graphs. In Proc. the 9th IEEE Symp. Inform. Visualization, October 2003, pp.51-58.

[35] Martins R M, Andery G F, Heberle H et al. Multidimensional projections for visual analysis of social networks. Journal of Computer Science and Technology, 2012, 27(4): 791-810.

[36] Collins C, Penn G, Carpendale S. Bubble sets: Revealing set relations with isocontours over existing visualizations. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(6): 1009-1016.

[37] Leguay J, Lindgren A, Scott J, Friedman T, Crowcroft J. Opportunistic content distribution in an urban setting. In Proc. the 2006 SIGCOMM Workshop on Challenged Networks, September 2006, pp.205-212.

[38] Wagstaff K, Cardie C. Clustering with instance-level constraints. In Proc. the 17th Int. Conf. Machine Learning, June 29-July 2, 2000, pp.1103-1110.
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[1] Zhu Mingyuan;. Two Congruent Semantics for Prolog with CUT[J]. , 1990, 5(1): 82 -91 .
[2] Zhang Bo; Zhang Ling;. A Relation Matrix Approach to Labelling Temporal Relations in Scheduling[J]. , 1991, 6(4): 339 -346 .
[3] Harald E. Otto;. UNDO, An Aid for Explorative Learning?[J]. , 1992, 7(3): 226 -236 .
[4] Shen Yidong;. Form alizing Incomplete Knowledge in Incomplete Databases[J]. , 1992, 7(4): 295 -304 .
[5] Li Renwei; He Pei; Zhang Wenhui;. An Introduction to IN CAPS System[J]. , 1993, 8(1): 26 -37 .
[6] Zhao Zhaokeng; Dai Jun; Chen Wendan;. Automated Theorem Proving in Temporal Logic:T-Resolution[J]. , 1994, 9(1): 53 -62 .
[7] Jose K- Raphel; Siu Cheung Hui; Angela Goh;. Class Based Contextual Logic for DOOD[J]. , 1996, 11(2): 161 -170 .
[8] Chi-Ming CHUNG; Ding-An CHIANG; YANG Qing;. A Comparative Analysis of Different Arbitration Protocols for Multiple-Bus Multiprocessors[J]. , 1996, 11(3): 313 -325 .
[9] Ma Zongmin; Yan Li;. Using Multivalued Logic in Relational Database Containing Null Value[J]. , 1996, 11(4): 421 -426 .
[10] Shuai Dianxun;. Concurrent Competitive Wave Approach to Hyper-Distributed Hyper-Parallel AI Processing[J]. , 1997, 12(6): 543 -554 .

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