Abstract
Link prediction is one of the fundamental problems in complex networks. In this paper, we focus on link prediction in document networks, in which nodes are text documents. We propose the relational topic factorization model (RTF), a model that combines topic models and matrix factorization. We also develop an efficient Monte Carlo EM algorithm for learning the parameters. Empirical results show that our model outperforms other state-of-the-art ones, and can give better understanding of the documents.
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., Fienberg, S.E., Xing, E.P.: Mixed Membership Stochastic Blockmodels. Journal of Machine Learning Research 9, 33–40 (2008)
Balasubramanyan, R., Cohen, W.W. Block-lda: Jointly modeling entity-annotated text and entity-entity links. In: SDM, vol. 11, pp. 450–461. SIAM (2011)
Chang, J., Blei, D.M.: Relational topic models for document networks. In: International Conference on Artificial Intelligence and Statistics, pp. 81–88 (2009)
Chen, N., Zhu, J., Xia, F., Zhang, B.: Generalized relational topic models with data augmentation. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1273–1279. AAAI Press (2013)
Hoff, P.D., Raftery, A.E., Handcock, M.S.: Latent space approaches to social network analysis. Journal of the American Statistical Association 97(460), 1090–1098 (2002)
Hofman, J.M., Wiggins, C.H.: Bayesian approach to network modularity. Physical Review Letters 100(25), 258701 (2008)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE (2008)
Kemp, C., Griffiths, T.L., Tenenbaum, J.B.: Discovering latent classes in relational data
Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: International Conference on Information and Knowledge Management, pp. 556–559 (2003)
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)
Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: Proceedings of the 17th International Conference on World Wide Web, pp. 101–110. ACM (2008)
Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 437–452. Springer, Heidelberg (2011)
Nallapati, R., Cohen, W.W.: Link-plsa-lda: A new unsupervised model for topics and influence of blogs. In: ICWSM (2008)
Newman, M.E.J.: The Structure and Function of Complex Networks. Siam Review 45 (2003)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456. ACM (2011)
Wasserman, S., Pattison, P.: Logit models and logistic regressions for social networks: I. an introduction to markov graphs andp. Psychometrika 61(3), 401–425 (1996)
Xu, Z., Ke, Y., Wang, Y., Cheng, H., Cheng, J.: A model-based approach to attributed graph clustering. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 505–516. ACM (2012)
Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1151–1156. IEEE (2013)
Zhu, Y., Yan, X., Getoor, L., Moore, C.: Scalable text and link analysis with mixed-topic link models. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 473–481. ACM (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, W., Li, J., Yong, X. (2014). Relational Topic Factorization for Link Prediction in Document Networks. In: Bonato, A., Graham, F., Prałat, P. (eds) Algorithms and Models for the Web Graph. WAW 2014. Lecture Notes in Computer Science(), vol 8882. Springer, Cham. https://doi.org/10.1007/978-3-319-13123-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-13123-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13122-1
Online ISBN: 978-3-319-13123-8
eBook Packages: Computer ScienceComputer Science (R0)