document: Document hierarchies from text and links Hierarchical taxonomies provide a multi-level view of large document collections , allowing users to rapidly drill down to fine-grained distinctions in topics of interest . We show that automatically induced taxonomies can be made more robust by combining text with relational links . The underlying mechanism is a Bayesian generative model in which a latent hierarchical structure explains the observed data -- thus , finding hierarchical groups of documents with similar word distributions and dense network connections . As a nonparametric Bayesian model , our approach does not require pre-specification of the branching factor at each non-terminal , but finds the appropriate level of detail directly from the data . Unlike many prior latent space models of network structure , the complexity of our approach does not grow quadratically in the number of documents , enabling application to networks with more than ten thousand nodes . Experimental results on hypertext and citation network corpora demonstrate the advantages of our hierarchical , multimodal approach .
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