Abstract
In this paper, we introduce a generative model named Author-Topic-Community (ATC) model which can infer authors’ interests and their community structure at the same time based on the contents and citation information of a document corpus. Via the mutual promotion between the author topics and the author community structure introduced in the ATC model, the robustness of the model towards cases with spare citation information can be enhanced. Variational inference is adopted to estimate the model parameters of ATC. We performed evaluation using both synthetic data as well as a real dataset which contains SIGKDD and SIGMOD papers published in 10 years. By constrasting the performance of ATC with some state-of-the-art methods which model authors’ interests and their community structure separately, our experimental results show that 1) the ATC model with the inference of the authors’ interests and the community structure integrated can improve the accuracy of author topic modeling and that of author community discovery; and 2) more in-depth analysis of the authors’ influence can be readily supported.
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
Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pp. 487–494. AUAI Press (2004)
Zhou, D., Manavoglu, E., Li, J., Giles, C., Zha, H.: Probabilistic models for discovering e-communities. In: Proceedings of the 15th International World Wide Web Conference, pp. 173–182. ACM (2006)
Liu, Y., Niculescu-Mizil, A., Gryc, W.: Topic-link LDA: Joint models of topic and author community. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 665–672. ACM (2009)
Kataria, S., Mitra, P., Caragea, C., Giles, C.: Context sensitive topic models for author influence in document networks. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Nallapati, R., Ahmed, A., Xing, E., Cohen, W.: Joint latent topic models for text and citations. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 542–550. ACM (2008)
Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: Proceedings of the 17th International World Wide Web Conference, pp. 101–110. ACM (2008)
Bhattacharya, I., Getoor, L.: A latent dirichlet model for unsupervised entity resolution. Technical Reports of the Computer Science Department (2005)
Shiozaki, H., Eguchi, K., Ohkawa, T.: Entity Network Prediction Using Multitype Topic Models. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 705–714. Springer, Heidelberg (2008)
Tu, Y., Johri, N., Roth, D., Hockenmaier, J.: Citation author topic model in expert search. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 1265–1273. Association for Computational Linguistics (2010)
Minka, T.: Expectation propagation for approximate Bayesian inference. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 362–369. Morgan Kaufmann Publishers Inc. (2001)
Griffiths, T., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America 101(suppl. 1), 5228 (2004)
Lin, Y., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.: Analyzing communities and their evolutions in dynamic social networks. ACM Transactions on Knowledge Discovery from Data (TKDD) 3(2), 8 (2009)
Chang, J., Blei, D.: Relational topic models for document networks. In: Artificial Intelligence and Statistics, pp. 81–88 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, C., Cheung, W.K., Ye, Y., Zhang, X. (2012). The Author-Topic-Community Model: A Generative Model Relating Authors’ Interests and Their Community Structure. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-35527-1_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35526-4
Online ISBN: 978-3-642-35527-1
eBook Packages: Computer ScienceComputer Science (R0)