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Markov Random Field and Social Networks

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Book cover Virtual Communities, Social Networks and Collaboration

Part of the book series: Annals of Information Systems ((AOIS,volume 15))

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Abstract

A Markov random field (MRF) is composed of 2D or 3D Markov chains providing spatial homogeneity in some sense. Markov random fields are reviewed and investigated as models of that kind of models. In a matter of graphs, spatial interactions between nodes defining local conditionals in regions denote Markov networks. These models are the set of sites, with a certain probability structure to the possible labeling of those sites.

Physical properties of the neighbors could be explained by partial differential equation (PDE) inside the potential function introducing PDE-MRF models. In Bayesian analysis, they have been used to describe the local characteristics of the spatial interaction between sites introducing Bayesian networks. A social network is a social structure constructing nodes, which are connected by one or more specific types of interdependency. When the nodes are explained by conditional probability modeling between them, in that case a connection between MRF and social networks could be established, to identify the local connectivity.

In this work, spatial behavior of the MRF models in nonrectangular lattice would be investigated. MRF models called PDE-MRF models are introduced based on the total variation of the region, considering smoothness assumptions. Finally, the Markov assumptions to deal with social network models are generalized and are discussed; some classes of estimations have been introduced using the Gibbs sampler.

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References

  1. Anderson, C., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social Networks, 21, 37–66.

    Article  Google Scholar 

  2. Aykroyd, R., Haigh, J. G. B., & Zimeras, S. (1996). Unexpected spatial patterns in exponential family auto-models. Graphical Models and Image Processing, 58, 452–463.

    Article  Google Scholar 

  3. Aykroyd, R. G., & Green, P. J. (1991). Global and local priors and the location of lesions using gamma-camera imagery. Philosophical Transactions of the Royal Society Series A, 337, 323–342.

    Article  MATH  Google Scholar 

  4. Aykroyd, R. G., & Zimeras, S. (1999). Inhomogeneous prior models for image reconstruction. Journal of American Statistical Association (JASA), 94(447), 934–946.

    Article  MathSciNet  MATH  Google Scholar 

  5. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society: Series B, 36, 192–236.

    MathSciNet  MATH  Google Scholar 

  6. Besag, J. (1986). On the statistical analysis of dirty pictures (with discussion). Journal of the Royal Statistical Society: Series B, 48, 259–302.

    MathSciNet  MATH  Google Scholar 

  7. Chandler, D. (1978). Introduction to modern statistical mechanics. New York: Oxford University Press.

    Google Scholar 

  8. Cross, G. R., & Jain, A. K. (1983). Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(1), 25–39.

    Article  Google Scholar 

  9. Diggle, P. J. (1983). Statistical analysis of spatial pattern point. London: Academic Press.

    Google Scholar 

  10. Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832–842.

    Article  MathSciNet  MATH  Google Scholar 

  11. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.

    Article  MATH  Google Scholar 

  12. Green, P. J. (1990). Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Transactions on Medical Imaging, 9, 84–93.

    Article  Google Scholar 

  13. Green, P. J., & Han, X. L. (1992). Metropolis methods, Gaussian proposals and antithetic variables. Lecture Notes in Statistics, 74, 142–164.

    Article  MathSciNet  Google Scholar 

  14. Haindl, M. (1991). Texture synthesis. CWI Quarterly, 4, 305–331.

    MATH  Google Scholar 

  15. Hamersley, J. A., & Clifford, P. (1971). Markov fields on finite graphs and lattices. Unpublished work.

    Google Scholar 

  16. Hastings, W. K. (1970). Monte Carlo simulation methods using Markov chains, and their applications. Biometrika, 57, 97–109.

    Article  MATH  Google Scholar 

  17. Ising, E. (1925). Beitrag zur Theorie des Ferromagnetismus. Zeitschrift für Physik, 31, 253–258.

    Article  Google Scholar 

  18. Kindermann, R., & Snell, J. L. (1980). Markov random fields and their applications. Providence, RI: American Mathematical Society.

    Book  MATH  Google Scholar 

  19. Li, S. (1995). Markov random fields in computer vision. New York: Springer.

    Google Scholar 

  20. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1091.

    Article  Google Scholar 

  21. Poston, T., & Stewart, I. (1978). Catastrophe theory and its applications. London: Pitman.

    MATH  Google Scholar 

  22. Qian, W., & Titterington, D. M. (1991). Multidimensional Markov chain model for image texture. Journal of the Royal Statistical Society: Series B, 53, 661–674.

    MathSciNet  MATH  Google Scholar 

  23. Ranngarajan, A., & Chellappa, R. (1995). Markov random fields models in image processing. In M. Arbib (Ed.), The handbook of brain theory and neural networks (pp. 564–567). Cambridge, MA: MIT Press.

    Google Scholar 

  24. Ripley, B. D., & Sutherland, A. I. (1990). Finding spiral structures in images of galaxies. Philosophical Transactions of the Royal Society Series A, 332, 477–485.

    Article  Google Scholar 

  25. Smith, A. F. M., & Robert, G. O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B, 55, 3–23.

    MATH  Google Scholar 

  26. Spitzer, F. (1971). Markov random fields and Gibbs ensembles. The American Mathematical Monthly, 78, 142–154.

    Article  MathSciNet  MATH  Google Scholar 

  27. Taskar, B., Abbeel, P., & Koller, D. (2002, August). Discriminative probabilistic models for relational data. In Proceedings of the 18th conference on uncertainty in artificial intelligence (pp. 485–492). San Francisco: Morgan Kaufmann.

    Google Scholar 

  28. Wasserman, S., & Pattison, P. (1996). Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p*. Psychometrika, 61, 401–425.

    Article  MathSciNet  MATH  Google Scholar 

  29. Weir, I. S. (1993). Statistical modeling and reconstructions in single photons emission computed tomography. Ph.D. thesis, Bristol University, Bristol, UK.

    Google Scholar 

  30. Zhang, J. (1992). The mean field theory in EM procedures for Markov random fields. IEEE Transactions on Signal Processing, 40(10), 2570–2583.

    Article  MATH  Google Scholar 

  31. Zimeras, S. (1997). Statistical models in medical image analysis. Ph.D. thesis, Leeds University, Leeds, UK.

    Google Scholar 

  32. Zimeras, S. (2006). Simulating texture patterns using auto-logistic models. WSEAS Transactions on Systems, 5(10), 2269–2276.

    Google Scholar 

  33. Zimeras, S., & Georgiakodis, F. (2005). Bayesian models for medical image biology using Monte Carlo Markov chain techniques. Mathematical and Computer Modeling, 42(2005), 759–768.

    Article  MATH  Google Scholar 

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Correspondence to Stelios Zimeras .

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Zimeras, S. (2012). Markov Random Field and Social Networks. In: Lazakidou, A. (eds) Virtual Communities, Social Networks and Collaboration. Annals of Information Systems, vol 15. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3634-8_11

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  • DOI: https://doi.org/10.1007/978-1-4614-3634-8_11

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-3633-1

  • Online ISBN: 978-1-4614-3634-8

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