Locating Hidden Groups in Communication Networks Using Hidden Markov Models

  • Malik Magdon-Ismail
  • Mark Goldberg
  • William Wallace
  • David Siebecker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2665)

Abstract

A communication network is a collection of social groups that communicate via an underlying communication medium (for example newsgroups over the Internet). In such a network, a hidden group may try to camoflauge its communications amongst the typical communications of the network. We study the task of detecting such hidden groups given only the history of the communications for the entire communication network. We develop a probabilistic approach using a Hidden Markov model of the communication network. Our approach does not require the use of any semantic information regarding the communications. We present the general probabilistic model, and show the results of applying this framework to a simplified society. For 50 time steps of communication data, we can obtain greater than 90% accuracy in detecting both whether or not their is a hidden group, and who the hidden group members are.

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References

  1. 1.
    Monge, P., Contractor, N.: Theories of Communication Networks. Oxford University Press (2002)Google Scholar
  2. 2.
    Carley, K., Prietula, M., eds.: Computational Organization Theory. Lawrence Erlbaum associates, Hillsdale, NJ (2001)Google Scholar
  3. 3.
    Sanil, A., Banks, D., Carley, K.: Models for evolving fixed node networks: Model fitting and model testing. Journal oF Mathematical Sociology 21 (1996) 173–196CrossRefGoogle Scholar
  4. 4.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77 (1989) 257–286CrossRefGoogle Scholar
  5. 5.
    Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. IEEE ASSP Magazine (1986) 4–15Google Scholar
  6. 6.
    Georgeff, M.P., Wallace, C.S.: A general selection criterion for inductive inference. European Conference on Artificial Intelligence (ECAI, ECAI84) (1984) 473–482Google Scholar
  7. 7.
    Bunke, H., Caelli, T., eds.: Hidden Markov Models. Series in Machine Perception and Artificial Intelligence — Vol. 45. World Scientific (2001)Google Scholar
  8. 8.
    Edgoose, T., Allison, L.: MML Markov classification of sequential data. Stats. and Comp. 9 (1999) 269–278CrossRefGoogle Scholar
  9. 9.
    Allison, L., Wallace, C.S., Yee, C.N.: Finite-state models in the alignment of macro-molecules. J. Molec. Evol. 35 (1992) 77–89CrossRefGoogle Scholar
  10. 10.
    Allison, L., Wallace, C.S., Yee, C.N.: Normalization of affine gap costs used in optimal sequence alignment. J. Theor. Biol. 161 (1993) 263–269CrossRefGoogle Scholar
  11. 11.
    Bystroff, C., Thorsson, V., Baker, D.: HMMSTR: A hidden Markov model for local sequence-structure correlations in proteins. Journal of Molecular Biology 301 (2000) 173–90CrossRefGoogle Scholar
  12. 12.
    Bystroff, C., Baker, D.: Prediction of local structure in proteins using a library of sequence-structure motifs. J Mol Biol 281 (1998) 565–77CrossRefGoogle Scholar
  13. 13.
    Bystroff, C., Shao, Y.: Fully automated ab initio protein structure prediction using I-sites, HMMSTR and ROSETTA. Bioinformatics 18 (2002) S54–S61Google Scholar
  14. 14.
    Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge, MA (1998)Google Scholar
  15. 15.
    Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological Sequence Analysis. Cambridge, new York (2001)Google Scholar
  16. 16.
    Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press (1994)Google Scholar
  17. 17.
    Watts, D.J.: Small Worlds: The dynamics of networks between order and randomness. Princeton University Press, Princeton, NJ (1999)Google Scholar
  18. 18.
    Butler, B.: The dynamics of cyberspace: Examing and modelling online social structure. Technical report, Carnegie Melon University, Pittsburgh, PA (1999)Google Scholar
  19. 19.
    Carley, K., Wallace, A.: Computational organization theory: A new perspective. In Gass, S., Harris, C., eds.: Encyclopedia of Operations Research and Management Science. Kluwer Academic Publishers, Norwell, MA (2001)Google Scholar
  20. 20.
    Snijders, T.: The statistical evaluation of social network dynamics. In Sobel, M., Becker, M., eds.: Sociological Methodology dynamics. Basil Blackwell, Boston & London (2001) 361–395Google Scholar
  21. 21.
    Battiti, R.: Reactive search: Toward self-tuning heuristics. Modern Heuristic Search Methods, Chapter 4 (1996) 61–83Google Scholar
  22. 22.
    Battiti, R., Protasi, M.: Reactive local search for the maximum clique problem. Technical Report TR-95-052, Berkeley, ICSI, 1947 Center St. Suite 600 (1995)Google Scholar
  23. 23.
    Motwani, R., Raghavan, P.: Randomized Algorithms. Cambridge University Press, Cambridge, UK (2000)Google Scholar
  24. 24.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)Google Scholar
  25. 25.
    Aarts, E., Korst, J.: Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing. John Wiley & Sons Ltd., New York (1989)MATHGoogle Scholar
  26. 26.
    Stelmack, M., N., N., Batill, S.: Genetic algorithms for mixed discrete/continuous optimization in multidisciplinary design. In: AIAA Paper 98-4771, AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, St. Louis, Missouri (1998)Google Scholar
  27. 27.
    Siebeker, D., Goldberg, M., Magdon-Ismail, M., Wallace, W.: A Hidden Markov Model for describing the statistical evolution of social groups over communication networks. Technical report, Rensselaer Polytechnic Institute (2003) Forthcoming.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Malik Magdon-Ismail
    • 1
  • Mark Goldberg
    • 1
  • William Wallace
    • 2
  • David Siebecker
    • 1
  1. 1.CS DepartmentRPITroyUSA
  2. 2.DSES DepartmentRPITroyUSA

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