2013 ScalableTextandLinkAnalysiswith

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Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on such data sets, and make predictions and recommendations, it is useful to have models that are able to capture the processes which generate the text at each node and the links between them. In this paper, we combine classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community. The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm. We test our model on three data sets, performing unsupervised topic classification and link prediction. For both tasks, our model outperforms several existing state-of-the-art methods, achieving higher accuracy with significantly less computation, analyzing a data set with 1.3 million words and 44 thousand links in a few minutes.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 ScalableTextandLinkAnalysiswith	Cristopher Moore Xiaoran Yan Yaojia Zhu Lise Getoor			Scalable Text and Link Analysis with Mixed-topic Link Models				10.1145/2487575.2487693		2013