3. The method of claim 1 , comprising applying non-negative matrix factorization to the content link model.
1. A method for characterizing a corpus of documents each having one or more links, comprising: forming a Bayesian network using the documents; determining a Bayesian network structure using the one or more links; generating a content link model where the model is a generative probabilistic model of the corpus along with citation information among documents, each document represented as a mixture over latent topics, and each relationship among documents is modeled by another generative process with a topic distribution of each document being a mixture of distributions associated with related documents; using a citation-topic (CT) model with a generative process for each word w in the document d in the corpus, with document probabilities Ξ, topic distribution matrix Θ and word probabilities matrix Ψ, including: choosing a related document c from p (c|d,Ξ), a multinomial probability conditioned on the document d; choosing a topic z from the topic distribution of the document c, p(z|c,Θ); choosing a word w which follows the multinomial distribution p(w|z,Ψ) conditioned on the topic z; and determining one or more topics in the corpus and topic distribution for each document wherein the content link model captures direct and indirect relationships represented by the links.