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Multi-Relational Characterization of Dynamic Social Network Communities

  • Yu-Ru LinEmail author
  • Hari Sundaram
  • Aisling Kelliher
Chapter

Abstract

The emergence of the mediated social web – a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies – has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of “community”. The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people’s behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.

Keywords

Community Membership Social Media Data Community Discovery Core Tensor Dynamic Social Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This material is based upon work supported in part by NEC Labs America, an IBM Ph.D. Fellowship and a Kauffman Entrepreneur Scholarship. We are pleased to acknowledge Yun Chi, Shenghuo Zhu, Belle Tseng, Jun Tatemura and Koji Hino, from NEC Labs America, for providing the invaluable advices on community discovery and the NEC Blog dataset. We are indebted to Jimeng Sun, Paul Castro and Ravi Konuru, from IBM T.J. Watson Research Center, for providing advices on tensor analysis and the IBM enterprise data.

References

  1. 1.
    J. Brown, A. Collins, et al. (1989). Situated Cognition and the Culture of Learning. Educational Researcher 18(1): 32.Google Scholar
  2. 2.
    J. Lemke (1997). Cognition, Context, and Learning: A social Semiotic Perspective. In: Situated Cognition: Social, Semiotic, and Psychological Perspectives. Erlbaum, Mahwah, NJ, pp 37–56.Google Scholar
  3. 3.
    M. Granovetter (1985). Economic Action and Social Structure: A Theory of Embeddedness. American Journal of Sociology 91(3): 481–510.CrossRefGoogle Scholar
  4. 4.
    N. Friedkin and E. Johnsen (1999). Social Influence Networks and Opinion Change. Advances in Group Processes 16: 1–29.Google Scholar
  5. 5.
    L. Backstrom, D. Huttenlocher, et al. (2006). Group Formation in Large Social Networks: Membership, Growth, and Evolution. SIGKDD, 44–54, 2006.Google Scholar
  6. 6.
    S. Wasserman and K. Faust (1994). Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge.Google Scholar
  7. 7.
    L. Backstrom, R. Kumar, et al. (2008). Preferential Behavior in Online Groups. Proceedings of the International Conference on Web Search and Web Data Mining, 117–128.Google Scholar
  8. 8.
    J. M. Kleinberg (1999). Authoritative Sources in a Hyperlinked Environment. Journal of the ACM 46(5): 604–632.zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    S. Brin and L. Page (1998). The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems 30(1–7): 107–117.CrossRefGoogle Scholar
  10. 10.
    J. Shi and J. Malik (2000). Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8): 888–905.CrossRefGoogle Scholar
  11. 11.
    T. Kolda and J. Sun (2008). Scalable Tensor Decompositions for Multi-aspect Data Mining. ICDM, 2008.Google Scholar
  12. 12.
    H. Rheingold (1899). Virtual Community: Homesteading on the Electronic Frontier. MIT Press, London.Google Scholar
  13. 13.
    Q. Jones (1997). Virtual-Communities, Virtual Settlements & Cyber-Archaeology: A Teoretical Outline. Journal of Computer Mediated Communication 3(3): 35–49.Google Scholar
  14. 14.
    H. Garfinkel (1984). Studies in Ethnomethodology. Polity Press, Cambridge.Google Scholar
  15. 15.
    P. Dourish (2001). Where the Action Is: The Foundations of Embodied Interaction. MIT Press, Cambridge.Google Scholar
  16. 16.
    D. Garlaschelli and M. Loffredo (2004). Patterns of Link Reciprocity in Directed Networks. Physical Review Letters 93(26): 268701.Google Scholar
  17. 17.
    M. Granovetter (1973). The Strength of Weak Ties. American Journal of Sociology 78(6): 1360.Google Scholar
  18. 18.
    Y.-R. Lin, H. Sundaram, et al. (2006). Discovery of Blog Communities Based on Mutual Awareness. The 3rd Annual Workshop on the Weblogging Ecosystems: Aggregation, Analysis and Dynamics.Google Scholar
  19. 19.
    J. Travers and S. Milgram (1969). An Experimental Study of the Small World Problem. Sociometry 32(4): 425–443.CrossRefGoogle Scholar
  20. 20.
    Y.-R. Lin, H. Sundaram, et al. (2007). Blog Community Discovery and Evolution Based on Mutual Awareness Expansion. IEEE/WIC/ACM International Conference on Web Intelligence, 2007.Google Scholar
  21. 21.
    F. Chung and S. Yau (2000). Discrete Green’s Functions. Journal of Combinatorial Theory (A) 91(1–2): 191–214.zbMATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    I. Dhillon, Y. Guan, et al. (2005). A Unified View of Kernel k-Means, Spectral Clustering and Graph Partitioning. Technical Report. University of Texas, Austin.Google Scholar
  23. 23.
    R. Kannan, S. Vempala, et al. (2004). On Clusterings: Good, Bad and Spectral. Journal of the ACM 51(3): 497–515.zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    M. Sahami and T. Heilman (2006). A web-based kernel function for measuring the similarity of short text snippets. Proceedings of the 15th International Conference on World Wide Web, 377–386, 2006.Google Scholar
  25. 25.
    S. Asur, S. Parthasarathy, et al. (2007). An Event-based Framework for Characterizing the Evolutionary Behavior of Interaction Graphs. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Google Scholar
  26. 26.
    G. Palla, A. Barabasi, et al. (2007). Quantifying Social Group Evolution. Nature 446: 664–667.CrossRefGoogle Scholar
  27. 27.
    D. Chakrabarti, R. Kumar, et al. (2006). Evolutionary Clustering. SIGKDD, 554–560.Google Scholar
  28. 28.
    Y. Chi, X. Song, et al. (2007). Evolutionary Spectral Clustering by Incorporating Temporal Smoothness. SIGKDD.Google Scholar
  29. 29.
    K. Yu, S. Yu, et al. (2005). Soft Clustering on Graphs. NIPS’05.Google Scholar
  30. 30.
    Y.-R. Lin, Y. Chi, et al. (2009). Analyzing Communities and Their Evolutions in Dynamics Networks. Transactions on Knowledge Discovery from Data (TKDD) 3(2): 1–31.CrossRefGoogle Scholar
  31. 31.
    J. Moody and D. White (2003). Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups. American Sociological Review 68: 103–127.CrossRefGoogle Scholar
  32. 32.
    A. Agostino (1999). The Relevance of Media as Artifact: Technology Situated in Context. Educational Technology & Society 2(4): 46–52.Google Scholar
  33. 33.
    J. Carroll and J. Chang (1970). Analysis of Individual Differences in Multidimensional Scaling Via an N-way Generalization of “Eckart-Young” Decomposition. Psychometrika 35(3): 283–319.zbMATHCrossRefGoogle Scholar
  34. 34.
    R. Harshman (1970). Foundations of the PARAFAC Procedure: Models and Conditions for an “Explanatory” Multi-Modal Factor Analysis. UCLA Working Papers in Phonetics 16(1): 84.Google Scholar
  35. 35.
    A. Popescul, L. H. Ungar, et al. (2001). Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. UAI 2001, 437–444.Google Scholar
  36. 36.
    D. Lee and H. Seung (2001). Algorithms for Non-Negative Matrix Factorization. NIPS, 556–562, 2001.Google Scholar
  37. 37.
    Y.-R. Lin, J. Sun, et al. (2009). MetaFac: Community Discovery via Relational Hypergraph Factorization. SIGKDD, 2009.Google Scholar
  38. 38.
    Y.-R. Lin, Y. Chi, et al. (2008). FaceNet: A Framework for Analyzing Communities and Their Evolutions in Dynamics Networks. Proceedings of the 17th International World Wide Web Conference, 2008.Google Scholar
  39. 39.
    T. Hofmann (1999). Probabilistic Latent Semantic Indexing. SIGIR, 1999.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Arts, Media and EngineeringArizona State UniversityTempeUSA

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