Topics over Time: A non-markov continuous-time model of topical trends

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 2006, Issue , 2006, Pages 424-433

  Wang, Xuerui ^a   McCallum, Andrew ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Graphical Models; Temporal Analysis; Topic Modeling

Indexed keywords

DISCRETIZATION; GRAPHICAL MODELS; TEMPORAL ANALYSIS; TOPIC MODELING;

CORRELATION METHODS; DATA ACQUISITION; DATA STRUCTURES; DISCRETE TIME CONTROL SYSTEMS; MARKOV PROCESSES;

DATA MINING;

EID: 33749565782     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. C. Andrieu, N. de Freitas, A. Doucet, and M. Jordan. An introduction to MCMC for machine learning. Machine Learning, 50:5-43, 2003.
2. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
3. D. M. Blei and J. D. Lafferty. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning, 2006.
4. E. Erosheva, S. Fienberg, and J. Lafferty. Mixed membership models of scientific publications. Proceedings of the National Academy of Sciences, 101(Suppl. 1), 2004.
5. T. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl. 1):5228-5235, 2004.
6. T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum. Integrating topics and syntax. In Advances in Neural Information Processing Systems (NIPS) 17, 2004.
7. J. Kleinberg. Bursty and hierarchical structure in streams. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002.
8. P. Kumaraswamy. A generalized probability density function for double-bounded random processes. Journal of Hydrology, 46:79-88, 1980.
9. R. E. Madsen, D. Kauchak, and C. Elkan. Modeling word burstiness using the Dirichlet distribution. In Proceedings of the 22nd International Conference on Machine Learning, 2005.
10. A. McCallum, A. Corrada-Emanuel, and X. Wang. Topic and role discovery in social networks. In Proceedings of 19th International Joint Conference on Artificial Intelligence, 2005.
11. U. Nodelman, C. Shelton, and D. Koller. Continuous time Bayesian networks. In Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, pages 378-387, 2002.
12. M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, 2004.
13. P. Sarkar and A. Moore. Dynamic social network analysis using latent space models. In The 19th Annual Conference on Neural Information Processing Systems, 2005.
14. X. Song, C.-Y. Lin, B. L. Tseng, and M.-T. Sun. Modeling and predicting personal information dissemination behavior. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005.
15. R. Swan and D. Jensen. Timemines: Constructing timelines with statistical models of word usage. In The 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Workshop on Text Mining, pages 73-80, 2000.
16. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical Dirichlet processes. Technical report, UC Berkeley Statistics TR-653, 2004.
17. X. Wang and A. McCallum. A note on topical n-grams. Technical report, UMass UM-CS-2005-071, 2005.
18. X. Wang, N. Mohanty, and A. McCallum. Group and topic discovery from relations and text. In The 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Workshop on Link Discovery: Issues, Approaches and Applications, pages 28-35, 2005.