Abstract
We present a probabilistic generative model of entity relationships and textual attributes; the model simultaneously discovers groups among the entities and topics among the corresponding text. Block models of relationship data have been studied in social network analysis for some time, however here we cluster in multiple modalities at once. Significantly, joint inference allows the discovery of groups to be guided by the emerging topics, and vice-versa. We present experimental results on two large data sets: sixteen years of bills put before the U.S. Senate, comprising their corresponding text and voting records, and 43 years of similar data from the United Nations. We show that in comparison with traditional, separate latent-variable models for words or block structures for votes, our Group-Topic model’s joint inference improves both the groups and topics discovered. Additionally, we present a non-Markov continouous-time group model to capture shifting group structure over time.
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
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Carley, K.: A comparison of artificial and human organizations. Journal of Economic Behavior and Organization 56, 175–191 (1996)
Denham, W.W., McDaniel, C.K., Atkins, J.R.: Aranda and Alyawarra kinship: A quantitative argument for a double helix model. American Ethnologist 6(1), 1–24 (1979)
Sparrow, M.: The application of network analysis to criminal intelligence: an assessment of prospects. Social Networks 13, 251–274 (1991)
Hix, S., Noury, A., Roland, G.: Power to the parties: Cohesion and competition in the European Parliament, 1979-2001. British Journal of Political Science 35(2), 209–234 (2005)
Cox, G., Poole, K.: On measuring the partisanship in roll-call voting: The U.S. House of Represenatatives, 1887-1999. American Journal of Political Science 46(1), 477–489 (2002)
Kubica, J., Moore, A., Schneider, J., Yang, Y.: Stochastic link and group detection. In: Proceedings of the 17th National Conference on Artificial Intelligence (2002)
Bhattacharya, I., Getoor, L.: Deduplication and group detection using links. In: Workshop on Link Analysis and Group Detection, in conjunction with the 10th International ACM SIGKDD Conference (2004)
Nowicki, K., Snijders, T.A.: Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 96(455) (2001)
Kemp, C., Griffiths, T.L., Tenenbaum, J.: Discovering latent classes in relational data. Technical report, MIT CSAIL (2004)
McCallum, A., Corrada-Emanuel, A., Wang, X.: Topic and role discovery in social networks. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (2005)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Erosheva, E.A., Fienberg, S.E., Lafferty, J.: Mixed-membership models of scientific publications. Proceedings of the National Academy of Sciences 97(22), 11885–11892 (2004)
Airoldi, E.M., Blei, D.M., Xing, E.P., Fienberg, S.E.: A latent mixed-membership model for relational data. In: Workshop on Link Discovery: Issues, Approaches and Applications, in conjunction with the 11th International ACM SIGKDD Conference (2005)
Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003)
Bekkerman, R., Yaniv, R.E., McCallum, A.: Multi-way distributional clustering via pairwise interactions. In: Proceedings of the 22nd International Conference on Machine Learning (2005)
Jakulin, A., Buntine, W.: Analyzing the US Senate in 2003: Similarities, networks, clusters and blocs (2004)
Pajala, A., Jakulin, A., Buntine, W.: Parliamentary group and individual voting behavior in Finnish Parliamentin year 2003: A group cohesion and voting similarity analysis (2004)
Beeferman, D., Berger, A.: Agglomerative clustering of a search engine query log. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2000)
Fenn, D., Suleman, O., Efstathiou, J., Johnson, N.: How does Europe make its mind up? Connections, cliques, and compatibility between countries in the Eurovision song contest. arXiv:physics/0505071 (2005)
Voeten, E.: Documenting votes in the UN General Assembly (2003), http://home.gwu.edu/~voeten/UNVoting.htm
Sarkar, P., Moore, A.: Dynamic social network analysis using latent space models. In: Advances in Neural Information Processing Systems, vol. 18 (2005)
Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning (2006)
Wang, X., McCallum, A.: Topics over time: A non-markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
McCallum, A., Wang, X., Mohanty, N. (2007). Joint Group and Topic Discovery from Relations and Text. In: Airoldi, E., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds) Statistical Network Analysis: Models, Issues, and New Directions. ICML 2006. Lecture Notes in Computer Science, vol 4503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73133-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-73133-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73132-0
Online ISBN: 978-3-540-73133-7
eBook Packages: Computer ScienceComputer Science (R0)