Latent topic models for hypertext

2010

2009

Abstract We develop the relational topic model (RTM), a model of documents and the links between them. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them.

2001

2010

2008

In this paper we demonstrate the applicability of latent Dirichlet allocation (LDA) for classifying large Web document collections. One of our main results is a novel influence model that gives a fully generative model of the document content taking linkage into account. In our setup, topics propagate along links in such a way that linked documents directly influence the words in the linking document. As another main contribution we develop LDA specific boosting of Gibbs samplers resulting in a significant speedup in our experiments. The inferred LDA model can be applied for classification as dimensionality reduction similarly to latent semantic indexing. In addition, the model yields link weights that can be applied in algorithms to process the Web graph; as an example we deploy LDA link weights in stacked graphical learning. By using Weka's BayesNet classifier, in terms of the AUC of classification, we achieve 4% improvement over plain LDA with BayesNet and 18% over tf.idf wit...

2008

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