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In this paper we demonstrate the applicability of latent Dirichlet allocation(LDA) for classifying large Web document collections. One of our main resultsis a novel influence model that gives a fully generative model of the documentcontent taking linkage into account. In our setup, topics propagate along linksin such a way that linked documents directly influence the words in the linkingdocument. As another main contribution we develop LDA specific boosting ofGibbs samplers resulting in a significant speedup in our experiments. Theinferred LDA model can be applied for classification as dimensionalityreduction similarly to latent semantic indexing. In addition, the model yieldslink weights that can be applied in algorithms to process the Web graph; as anexample we deploy LDA link weights in stacked graphical learning. By usingWeka's BayesNet classifier, in terms of the AUC of classification, we achieve4% improvement over plain LDA with BayesNet and 18% over tf.idf with SVM. OurGibbs sampling strategies yield about 5-10 times speedup with less than 1%decrease in accuracy in terms of likelihood and AUC of classification.

Large scale link based latent Dirichlet allocation for web document classification