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Community Discovery in Heterogeneous Social Networks

  • Lei MengEmail author
  • Ah-Hwee Tan
  • Donald C. Wunsch II
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a priori, do not address the weighting problem for fusing heterogeneous types of links, and have a heavy computational cost. This chapter studies the commonly used social links of users and explores the feasibility of the proposed heterogeneous data co-clustering algorithm GHF-ART, as introduced in Sect.  3.6, for discovering user communities in social networks. Contrary to the existing algorithms proposed for this task, GHF-ART performs real-time matching of patterns and one-pass learning, which guarantees its low computational cost. With a vigilance parameter to restrain the intra-cluster similarity , GHF-ART does not need the number of clusters a priori. To achieve a better fusion of multiple types of links, GHF-ART employs a weighting algorithm, called robustness measure (RM) , to incrementally assess the importance of all the feature channels for the representation of data objects of the same class. Extensive experiments have been conducted on two social network datasets to analyze the performance of GHF-ART. The promising results compare GHF-ART with existing methods and demonstrate the effectiveness and efficiency of GHF-ART. The content of this chapter is summarized and extended from [11] (Copyright ©2014 Society for Industrial and Applied Mathematics. Reprinted with permission. All rights reserved).

References

  1. 1.
    Bickel S, Scheffer T (2004) Multi-view clustering. In: ICDM, pp 19–26Google Scholar
  2. 2.
    Bisson G, Grimal C (2012) Co-clustering of multi-view datasets: a parallelizable approach. In: ICDM, pp 828–833Google Scholar
  3. 3.
    Carpenter GA, Grossberg S, Rosen DB (1991) Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4:759–771CrossRefGoogle Scholar
  4. 4.
    Chaudhuri K, Kakade SM, Livescu K, Sridharan K (2009) Multi-view clustering via canonical correlation analysis. In: ICML, pp 129–136Google Scholar
  5. 5.
    Chen Y, Wang L, Dong M (2010) Non-negative matrix factorization for semisupervised heterogeneous data coclustering. TKDE 22(10):1459–1474CrossRefGoogle Scholar
  6. 6.
    Dong Y, Tang J, Wu S, Tian J, Chawla NV, Rao J, Cao H (2012) Link prediction and recommendation across heterogeneous social networks. In: ICDM, pp 181–190Google Scholar
  7. 7.
    Drost I, Bickel S, Scheffer T (2006) Discovering communities in linked data by multi-view clustering. From data and information analysis to knowledge engineering. Springer, Berlin, pp 342–349Google Scholar
  8. 8.
    He J, Tan AH, Tan CL, Sung SY (2003) On quantitative evaluation of clustering systems. Clustering and information retrieval. Kluwer Academic Publishers, Netherlands, pp 105–133Google Scholar
  9. 9.
    Kumar AIII, Daumé H (2011) A co-training approach for multi-view spectral clustering. In: ICML, pp 393–400Google Scholar
  10. 10.
    Long B, Wu X, Zhang Z, Yu PS (2006) Spectral clustering for multi-type relational data. In: ICML, pp 585–592Google Scholar
  11. 11.
    Meng L, Tan AH (2014) Community discovery in social networks via heterogeneous link association and fusion. In: SIAM international conference on data mining (SDM), pp 803–811Google Scholar
  12. 12.
    Rege M, Dong M, Hua J (2008) Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering. In: Proceedings of international conference on world wide web, pp 317–326Google Scholar
  13. 13.
    Tang L, Wang X, Liu H (2009) Uncovering groups via heterogeneous interaction analysis. In: ICDM, pp 503–512Google Scholar
  14. 14.
    Tang W, Lu Z, Dhillon IS (2009) Clustering with multiple graphs. In: ICDM, pp 1016–1021Google Scholar
  15. 15.
    Wang X, Qian B, Ye J, Davidson I (2013) Multi-objective multi-view spectral clustering via Pareto optimization. In: SDM, pp 234–242Google Scholar
  16. 16.
    Wang X, Tang L, Gao H, Liu H (2010) Discovering overlapping groups in social media. In: ICDM, pp 569–578Google Scholar
  17. 17.
    Whang JJ, Sui X, Sun Y, Dhillon IS (2012) Scalable and memory-efficient clustering of large-scale social networks. In: ICDM, pp 705–714Google Scholar
  18. 18.
    Xu RII, Wunsch DC (2011) BARTMAP: a viable structure for biclustering. Neural Netw 24:709–716CrossRefGoogle Scholar
  19. 19.
    Yang J, Leskovec J (2012) Defining and evaluating network communities based on ground-truth. In: SDM, pp 745–754Google Scholar
  20. 20.
    Yang Y, Chawla N, Sun Y, Han J (2012) Predicting links in multi-relational and heterogeneous networks. In: ICDM, pp 755–764Google Scholar
  21. 21.
    Zhang K, Lo D, Lim EP, Prasetyo PK (2013) Mining indirect antagonistic communities from social interactions. Knowl Inf Syst 35(3):553–583CrossRefGoogle Scholar
  22. 22.
    Zhao Y, Karypis G (2001) Criterion functions for document clustering: experiments and analysis. Technical report, Department of Computer Science, University of MinnesotaGoogle Scholar
  23. 23.
    Zhou D, Burges CJC (2007) Spectral clustering and transductive learning with multiple views. In: ICML, pp 1159–1166Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.NTU-UBC Research Center of Excellence in Active Living for the Elderly (LILY)Nanyang Technological UniversitySingaporeSingapore
  2. 2.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Applied Computational Intelligence LaboratoryMissouri University of Science and TechnologyRollaUSA

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