Generalized relational topic models with data augmentation

Authors:
Ning Chen;Jun Zhu;Fei Xia;Bo Zhang
Affiliations:
Dept. of CS & T, TNList Lab, State Key Lab of ITS, Tsinghua University, Beijing, China;Dept. of CS & T, TNList Lab, State Key Lab of ITS, Tsinghua University, Beijing, China;School of Software, Tsinghua University, Beijing, China;Dept. of CS & T, TNList Lab, State Key Lab of ITS, Tsinghua University, Beijing, China
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

Relational topic models have shown promise on analyzing document network structures and discovering latent topic representations. This paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference with a regularization parameter to deal with the imbalanced link structure issue in common real networks; and 3) instead of doing variational approximation with strict mean-field assumptions, we present a collapsed Gibbs sampling algorithm for the generalized relational topic models without making restricting assumptions. Experimental results demonstrate the significance of these extensions on improving the prediction performance, and the time efficiency can be dramatically improved with a simple fast approximation method.