Abstract
Multiplex document networks have multiple types of links such as citation and coauthor links between scientific papers. Inferring thematic topics from multiplex document networks requires quantifying and balancing the influence from different types of links. It is therefore a problem of considerable interest and represents significant challenges. To address this problem, we propose a novel multiplex topic model (MTM) that represents the topic influence from different types of links using a factor graph. To estimate parameters in MTM, we also develop an approximate inference algorithm, multiplex belief propagation (MBP), which can estimate the influence weights of multiple links automatically at each learning iteration. Experimental results confirm the superiority of MTM in two applications, document clustering and link prediction, when compared with several state-of-the-art link-based topic models.
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References
Steyvers, M., Smyth, P., Zvi, M.R., Griffiths, T.: Probabilistic author-topic models for information discovery. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 306–315 (2004)
Chang, J., Blei, D.M.: Hierarchical relational models for document networks. The Annals of Applied Statistics 4, 124–150 (2010)
Zeng, J., Cheung, W.K.,Hung Li, C., Liu, J.: Multirelational topic models. In: ICDM, pp. 1070–1075 (2009)
Liu, Y., Niculescu-Mizil, A., Gryc, W.: Topic-link LDA: joint models of topic and author community. In: ICML, p. 84 (2009)
Sun, Y., Han, J., Gao, J., Yu, Y.: iTopicModel: Information network-integrated topic modeling. In: ICDM, pp. 493–502 (2009)
Kschischang, F.R., Frey, B.J., Loeliger, H.A.: Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 47(2), 498–519 (2001)
Zeng, J., Cheung, W.K., Liu, J.: Learning topic models by belief propagation. IEEE TPAMI abs/1109.3437 (2012)
Bishop, C.M.: Pattern recognition and machine learning. Springer (2006)
Blei, D.M., Ng, A., Jordan, M.: Latent dirichlet allocation. JMLR 3, 993–1022 (2003)
Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: WWW, pp. 101–110 (2008)
Deng, H., Han, J., Zhao, B., Yu, Y., Lin, C.X.: Probabilistic topic models with biased propagation on heterogeneous information networks. In: KDD, pp. 1271–1279 (2011)
Griffiths, T.L., Steyvers, M.: Finding Scientific Topics. PNAS 101 (suppl. 1), 5228–5235 (2004)
Heinrich, G.: Parameter estimation for text analysis. Technical report (2004)
Teh, Y.W., Newman, D., Welling, M.: A collapsed variational bayesian inference algorithm for latent dirichlet allocation. In: NIPS, pp. 1353–1360 (2006)
Zeng, J., Liu, Z.Q.: Markov random field-based statistical character structure modeling for handwritten Chinese character recognition. IEEE TPAMI 30(5), 1193–1198 (2008)
Elidan, G., McGraw, I., Koller, D.: Residual belief propagation: Informed scheduling for asynchronous message passing. In: UAI, pp. 165–173 (2006)
Zeng, J., Cao, X.-Q., Liu, Z.-Q.: Residual belief propagation for topic modeling. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS, vol. 7713, pp. 739–752. Springer, Heidelberg (2012)
Zeng, J., Liu, Z.Q., Cao, X.Q.: A new approach to speeding up topic modeling. CoRR abs/1204.0170 (2012)
McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automaticing the construction of internet portals with machine learning. Information Retrival 3(2), 127–163 (2000)
Zeng, J.: A topic modeling toolbox using belief propagation. JMLR 13, 2233–2236 (2012)
Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, 583–617 (2002)
Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Physical Review EÂ 69, 026113 (2004)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: KDD, pp. 448–456 (2011)
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Yang, J., Zeng, J., Cheung, W.K. (2013). Multiplex Topic Models. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_47
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DOI: https://doi.org/10.1007/978-3-642-37453-1_47
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