15. A method for extracting characteristics from a corpus of linked documents, comprising: deriving a content link model that explicitly captures direct and indirect relations represented by the links 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 extracting document topics and the topic distributions for all the documents in the corpus using the content-link model, wherein the content link model captures direct and indirect relationships represented by the links.
15. A method for extracting characteristics from a corpus of linked documents, comprising: deriving a content link model that explicitly captures direct and indirect relations represented by the links 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 extracting document topics and the topic distributions for all the documents in the corpus using the content-link model, wherein the content link model captures direct and indirect relationships represented by the links. 17. The method of claim 15 , comprising generating a Bayesian network for the documents that encodes the direct and indirect relations, wherein the relations are derived either explicitly from links or implicitly from the similarities among documents.