Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Multiplicative latent factor models for description and prediction of social networks
Computational & Mathematical Organization Theory
Temporal link prediction by integrating content and structure information
Proceedings of the 20th ACM international conference on Information and knowledge management
Generalized latent factor models for social network analysis
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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In this paper we address the problem of modelling relational data, which has appeared in many applications such as social network analysis, recommender systems and bioinformatics. Previous studies either consider latent feature based models to do link prediction in the relational data but disregarding local structure in the network, or focus exclusively on capturing network structure of objects based on latent blockmodels without coupling with latent characteristics of objects to avoid redundant information. To combine the benefits of the previous work, we model the relational data as a function of both latent feature factors and latent cluster memberships of objects via our proposed Latent Factor BlockModel (LFBM) to collectively discover globally predictive intrinsic properties of objects and capture the latent block structure. We also develop an optimization transfer algorithm to learn the latent factors. Extensive experiments on the synthetic data and several real world datasets suggest that our proposed LFBM model outperforms the state-of-the-art approaches for modelling the relational data.