document: Joint latent topic models for text and citations In this work , we address the problem of joint modeling of text and citations in the topic modeling framework . We present two different models called the Pairwise-Link-LDA and the Link-PLSA-LDA models . The Pairwise-Link-LDA model combines the ideas of LDA ( 4 ) and Mixed Membership Block Stochastic Models ( 1 ) and allows modeling arbitrary link structure . However , the model is computationally expensive , since it involves modeling the presence or absence of a citation ( link ) between every pair of documents . The second model solves this problem by assuming that the link structure is a bipartite graph . As the name indicates , Link-PLSA-LDA model combines the LDA and PLSA models into a single graphical model . Our experiments on a subset of Citeseer data show that both these models are able to predict unseen data better than the baseline model of Erosheva and Lafferty ( 8 ) , by capturing the notion of topical similarity between the contents of the cited and citing documents . Our experiments on two different data sets on the link prediction task show that the Link-PLSA-LDA model performs the best on the citation prediction task , while also remaining highly scalable . In addition , we also present some interesting visualizations generated by each of the models .
doc_bio_tags: O O B I O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O