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Social Network Discovery by Mining Spatio-Temporal Events

  • Hady W. Lauw
  • Ee-Peng Lim
  • HweeHwa Pang
  • Teck-Tim Tan
Article

Abstract

Knowing patterns of relationship in a social network is very useful for law enforcement agencies to investigate collaborations among criminals, for businesses to exploit relationships to sell products, or for individuals who wish to network with others. After all, it is not just what you know, but also whom you know, that matters. However, finding out who is related to whom on a large scale is a complex problem. Asking every single individual would be impractical, given the huge number of individuals and the changing dynamics of relationships. Recent advancement in technology has allowed more data about activities of individuals to be collected. Such data may be mined to reveal associations between these individuals. Specifically, we focus on data having space and time elements, such as logs of people's movement over various locations or of their Internet activities at various cyber locations. Reasoning that individuals who are frequently found together are likely to be associated with each other, we mine from the data instances where several actors co-occur in space and time, presumably due to an underlying interaction. We call these spatio-temporal co-occurrences events, which we use to establish relationships between pairs of individuals. In this paper, we propose a model for constructing a social network from events, and provide an algorithm that mines these events from the data. Experiments on a real-life data tracking people's accesses to cyber locations have also yielded encouraging results.

Keywords

data mining pattern discovery spatio-temporal analysis 

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References

  1. Adamic, L.A. and E. Adar (2003), “Friends and Neighbors on the Web,” Social Networks, 25(3), 211–230.CrossRefGoogle Scholar
  2. Agrawal, R., S. Rajagopalan, R. Srikant, and Y. Xu (2003), “Mining Newsgroups Using Networks Arising from Social Behavior,” in Proceedings of the 12th International World Wide Web Conference, Budapest, Hungary, pp. 688–703.Google Scholar
  3. Agrawal, R. and R. Srikant (1994), “Fast Algorithm for Mining Association Rules,” in Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, pp. 487–499.Google Scholar
  4. Agrawal, R. and R. Srikant (1995), “Mining Sequential Patterns,” in Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, pp. 3–14.Google Scholar
  5. Berry, M.W. and M. Browne (2005), “Email Surveillance Using Nonnegative Matrix Factorization,” in Proceedings of the Workshop on Link Analysis, Counterterrorism, and Security (in conj. with SIAM International Conference on Data Mining), Newport Beach, CA, USA, pp. 45–54.Google Scholar
  6. Boyd, D.M. (2004), “Friendster and Publicly Articulated Social Networking,” in Extended abstracts of the Conference on Human Factors and Computing Systems, Vienna, Austria, pp. 1279–1282.Google Scholar
  7. Carley, K. (1991), “A Theory of Group Stability,” American Sociological Review, 56(3), 331–354.Google Scholar
  8. Chapanond, A., M.S. Krishnamoorthy, and B. Yener (2005), “Graph Theoretic and Spectral Analysis of Enron Email Data,” in Proceedings of the Workshop on Link Analysis, Counterterrorism, and Security (in conj. with SIAM International Conference on Data Mining), Newport Beach, CA, USA, pp. 15–22.Google Scholar
  9. Das, G., K. Lin, H. Mannila, G. Renganathan, and P. Smyth (1998), “Rule Discovery from Time Series,” in Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 27–31.Google Scholar
  10. Diesner, J. and K.M. Carley (2005), “Exploration of Communication Networks from the Enron Email Corpus,” in Proceedings of the Workshop on Link Analysis, Counterterrorism, and Security (in conj. with SIAM International Conference on Data Mining), Newport Beach, CA, USA, pp. 3–14.Google Scholar
  11. Duan, Y., J. Wang, M. Kam, and J. Canny (2005), “A Secure Online Algorithm for Link Analysis on Weighted Graph,” in Proceedings of the Workshop on Link Analysis, Counterterrorism, and Security (in conj. with SIAM International Conference on Data Mining), Newport Beach, CA, USA, pp. 71–81.Google Scholar
  12. Faloutsos, C., K.S. McCurley, and A. Tomkins (2004), “Connection Subgraphs in Social Networks,” in Proceedings of the Workshop on Link Analysis, Counterterrorism, and Privacy (in conj. with SIAM International Conference on Data Mining), Lake Buena Vista, FA, USA.Google Scholar
  13. Keila, P.S. and D.B. Skillicorn (2005), “Structure in the Enron Email Dataset,” in Proceedings of the Workshop on Link Analysis, Counterterrorism, and Security (in conj. with SIAM International Conference on Data Mining), Newport Beach, CA, USA, pp. 55–64.Google Scholar
  14. Kempe, D., J. Kleinberg, and E. Tardos, (2003), “Maximizing the Spread of Influence through a Social Network,” in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C, USA, pp. 137–146.Google Scholar
  15. Koperski, K. and J. Han, (1995), “Discovery of Spatial Association Rules in Geographic Information Databases,” in Proceedings of the 4th International Symposium on Advances in Spatial Databases, Portland, Maine, USA, pp. 47—66.Google Scholar
  16. Krebs, V.E. (2002), “Mapping Networks of Terrorist Cells,” Connections, 24(3), 43–52.Google Scholar
  17. Kumar, R., J. Novak, P. Raghavan, and A. Tomkins (2004), “Structure and Evolution of Blogspace,” Communications of the ACM, 47(12), 35–39.CrossRefGoogle Scholar
  18. Lehmann, S. (2005), “Live and Dead Nodes,” in Proceedings of the Workshop on Link Analysis, Counterterrorism, and Security (in conj. with SIAM International Conference on Data Mining), Newport Beach, CA, USA, pp. 65–70.Google Scholar
  19. Lin, S. and H. Chalupsky (2003), “Unsupervised Link Discovery in Multi-Relational Data via Rarity Analysis,” in Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, FL, USA, pp. 171–178.Google Scholar
  20. Lu, H., L. Feng, and J. Han (2000), “Beyond Intratransaction Association Analysis: Mining Multidimensional Intertransaction Association Rules,” ACM Transactions on Information Systems, 18(4), 423–454.CrossRefGoogle Scholar
  21. Mannila, H., H. Toivonen, and A.I. Verkamo (1995), “Discovering Frequent Episodes in Sequences,” in Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining, Montreal, Canada, pp. 210–215.Google Scholar
  22. McCallum, A., A. Corrada-Emmanuel, and X. Wang (2005), “The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks, with Application to Enron and Academic Email,” in Proceedings of the Workshop on Link Analysis, Counterterrorism, and Security (in conj. with SIAM International Conference on Data Mining), Newport Beach, CA, USA, pp. 33–44.Google Scholar
  23. Mukherjee, M. and L.B. Holder (2004), “Graph-Based Data Mining on Social Networks,” in Workshop on Link Analysis and Group Detection (in conj. with the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), Seattle, WA, USA.Google Scholar
  24. Resig, J., S. Dawara, C.M. Homan, and A. Teredesai (2004), “Extracting Social Networks from Instant Messaging Populations,” in Workshop on Link Analysis and Group Detection (in conj. with the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), Seattle, WA, USA.Google Scholar
  25. Richardson, M. and P. Domingo (2002), “Mining Knowledge-Sharing Sites for Viral Marketing,” in Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp. 61–70.Google Scholar
  26. Schwartz, M.F. and D.C.M. Wood (1993), “Discovering Shared Interests Using Graph Analysis,” Communications of the ACM, 36(8), 78–89.CrossRefGoogle Scholar
  27. Shekhar, S., S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. Lu (1999), “Spatial Databases—Accomplishments and Research Needs,” IEEE Transactions on Knowledge and Data Engineering, 11(1), 45–55.CrossRefGoogle Scholar
  28. Shekhar, S. and Y. Huang (2001), “Discovering Spatial Co-Location Patterns: A Summary of Results,” in Proceedings of the 7th International Symposium on Spatial and Temporal Databases, Redondo Beach, CA, USA, pp. 236–256.Google Scholar
  29. Vlachos, M., G. Kollios, and D. Gunopulos (2002), “Discovering Similar Multidimensional Trajectories,” in Proceedings of the 18th International Conference on Data Engineering, San Jose, CA, USA, pp. 673–684.Google Scholar
  30. Wang, Y., E. Lim, and S. Hwang (2003), “On Mining Group Patterns of Mobile Users,” in Proceedings of the 14th International Conference on Database and Expert Systems Applications, Prague, Czech Republic, pp. 287–296.Google Scholar
  31. Wasserman, S. and K. Faust (1994), Social Network Analysis: Methods and Applications. Cambridge University Press.Google Scholar
  32. Xu, J. and H. Chen (2004), “Fighting Organized Crimes: Using Shortest-Path Algorithms to Identify Associations in Criminal Networks,” Decision Support Systems, 38(3), 473–487.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Hady W. Lauw
    • 1
  • Ee-Peng Lim
    • 1
  • HweeHwa Pang
    • 2
  • Teck-Tim Tan
    • 3
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.School of Information SystemsSingapore Management UniversitySingapore
  3. 3.Centre for IT ServicesNanyang Technological UniversitySingapore

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