Nonparametric latent feature models for link prediction

Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

250 Scopus citations

Abstract

As the availability and importance of relational data-such as the friendships summarized on a social networking website-increases, it becomes increasingly important to have good models for such data. The kinds of latent structure that have been considered for use in predicting links in such networks have been relatively limited. In particular, the machine learning community has focused on latent class models, adapting Bayesian nonparametric methods to jointly infer how many latent classes there are while learning which entities belong to each class. We pursue a similar approach with a richer kind of latent variable-latent features-using a Bayesian nonparametric approach to simultaneously infer the number of features at the same time we learn which entities have each feature. Our model combines these inferred features with known covariates in order to perform link prediction. We demonstrate that the greater expressiveness of this approach allows us to improve performance on three datasets.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
Pages1276-1284
Number of pages9
StatePublished - Dec 1 2009
Externally publishedYes
Event23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 - Vancouver, BC, Canada
Duration: Dec 7 2009Dec 10 2009

Publication series

NameAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Other

Other23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
CountryCanada
CityVancouver, BC
Period12/7/0912/10/09

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Miller, K. T., Griffiths, T. L., & Jordan, M. I. (2009). Nonparametric latent feature models for link prediction. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference (pp. 1276-1284). (Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference).