Abstract
Statistical relational learning (SRL) provides effective techniques to analyze social network data with rich collections of objects and complex networks. Infinite hidden relational models (IHRMs) introduce nonparametric mixture models into relational learning and have been successful in many relational applications. In this paper we explore the modeling and analysis of complex social networks with IHRMs for community detection, link prediction and product recommendation. In an IHRM-based social network model, each edge is associated with a random variable and the probabilistic dependencies between these random variables are specified by the model, based on the relational structure. The hidden variables, one for each object, are able to transport information such that non-local probabilistic dependencies can be obtained. The model can be used to predict entity attributes, to predict relationships between entities and it performs an interpretable cluster analysis. We demonstrate the performance of IHRMs with three social network applications. We perform community analysis on the Sampson’s monastery data and perform link analysis on the Bernard & Killworth data. Finally we apply IHRMs to the MovieLens data for prediction of user preference on movies and for an analysis of user clusters and movie clusters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Airoldi, E.M., Blei, D.M., Xing, E.P., Fienberg, S.E.: A latent mixed-membership model for relational data. In: Proc. ACM SIGKDD Workshop on Link Discovery (2005)
Aldous, D.: Exchangeability and related topics. In: Ecole d’Ete de Probabilites de Saint-Flour XIII 1983, pp. 1–198. Springer, Heidelberg (1985)
Antoniou, G., van Harmelen, F.: A Semantic Web Primer. MIT Press, Cambridge (2004)
Aukia, J., Kaski, S., Sinkkonen, J.: Inferring vertex properties from topology in large networks. In: NIPS 2007 workshop on statistical models of networks (2007)
Bernard, H., Killworth, P., Sailer, L.: Informant accuracy in social network data iv. Social Networks 2 (1980)
Blei, D., Jordan, M.: Variational inference for dp mixtures. Bayesian Analysis 1(1), 121–144 (2005)
Breiger, R.L., Boorman, S.A., Arabie, P.: An algorithm for clustering relational data with applications to social network analysis and comparison to multidimensional scaling. Journal of Mathematical Psychology 12 (1975)
Dzeroski, S., Lavrac, N. (eds.): Relational Data Mining. Springer, Berlin (2001)
Getoor, L., Friedman, N., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Dzeroski, S., Lavrac, N. (eds.) Relational Data Mining, Springer, Heidelberg (2001)
Getoor, L., Koller, D., Friedman, N.: From instances to classes in probabilistic relational models. In: Proc. ICML 2000 Workshop on Attribute-Value and Relational Learning (2000)
Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
Handcock, M.S., Raftery, A.E., Tantrum, J.M.: Model-based clustering for social networks. Journal of the Royal Statistical Society 170 (2007)
Hofmann, T., Puzicha, J.: Latent class models for collaborative filtering. In: Proc. 16th International Joint Conference on Artificial Intelligence (1999)
Ishwaran, H., James, L.: Gibbs sampling methods for stick breaking priors. Journal of the American Statistical Association 96(453), 161–173 (2001)
Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: Proc. 21st Conference on Artificial Intelligence (2006)
Neville, J., Jensen, D.: Leveraging relational autocorrelation with latent group models. In: Proc. 4th international workshop on Multi-relational mining, pp. 49–55. ACM Press, New York (2005)
Raedt, L.D., Kersting, K.: Probabilistic logic learning. SIGKDD Explor. Newsl. 5(1), 31–48 (2003)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proc. of the ACM 1994 Conference on Computer Supported Cooperative Work, pp. 175–186. ACM, New York (1994)
Sampson, F.S.: A Novitiate in a Period of Change: An Experimental and Case Study of Social Relationships. PhD thesis (1968)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender systems–a case study. In: WebKDD Workshop (2000)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommender algorithms for e-commerce. In: Proc. ACM E-Commerce Conference, pp. 158–167. ACM, New York (2000)
Sethuraman, J.: A constructive definition of dirichlet priors. Statistica Sinica 4, 639–650 (1994)
Wang, X., Mohanty, N., McCallum, A.: Group and topic discovery from relations and text. In: Proc. 3rd international workshop on Link discovery, pp. 28–35. ACM, New York (2005)
Xu, Z., Tresp, V., Yu, K., Kriegel, H.-P.: Infinite hidden relational models. In: Proc. 22nd UAI (2006)
Xu, Z., Tresp, V., Yu, S., Yu, K.: Nonparametric relational learning for social network analysis. In: Proc. 2nd ACM Workshop on Social Network Mining and Analysis, SNA-KDD 2008 (2008)
Yedidia, J., Freeman, W., Weiss, Y.: Constructing free-energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory 51(7), 2282–2312 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, Z., Tresp, V., Rettinger, A., Kersting, K. (2010). Social Network Mining with Nonparametric Relational Models. In: Giles, L., Smith, M., Yen, J., Zhang, H. (eds) Advances in Social Network Mining and Analysis. SNAKDD 2008. Lecture Notes in Computer Science, vol 5498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14929-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-14929-0_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14928-3
Online ISBN: 978-3-642-14929-0
eBook Packages: Computer ScienceComputer Science (R0)