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.
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. 13. The system of claim 1 , comprising a BPT (Bernoulli process topic) model with computer readable code to process an input document D ={d 1 ,d 2 , . . . ,d N } to generate variational parameters {γ i ,1 ≦i ≦N} and regenerative model parameters α,Λ, further comprising code to: Update ⁢ ⁢ the ⁢ ⁢ matrix ⁢ ⁢ V ⁢ ⁢ where ⁢ ⁢ V jl = exp ( Ψ ⁡ ( γ jl ) - Ψ ⁡ ( ∑ t = 1 K ⁢ ⁢ γ jt ) ) Update ⁢ ⁢ the ⁢ ⁢ matrix ⁢ ⁢ A ′ ⁢ ⁢ where ⁢ ⁢ A hg ′ = A hg [ Λ ⁢ ⁢ V T ⁢ Ξ ] hg Update ⁢ ⁢ γ ⁢ ⁢ where ⁢ ⁢ γ sl = α l + V sl ⁡ [ Ξ ⁢ ⁢ A ′ T ⁢ Λ ] sl Update ⁢ ⁢ the ⁢ ⁢ matrix ⁢ ⁢ V ⁢ ⁢ where ⁢ ⁢ V jl = exp ( Ψ ⁡ ( γ jl ) - Ψ ⁡ ( ∑ t = 1 K ⁢ ⁢ γ jt ) ) Update ⁢ ⁢ the ⁢ ⁢ matrix ⁢ ⁢ A ′ ⁢ ⁢ where ⁢ ⁢ A hg ′ = A hg [ Λ ⁢ ⁢ V T ⁢ Ξ ] hg Update ⁢ ⁢ the ⁢ ⁢ parameter ⁢ ⁢ Λ ⁢ ⁢ where ⁢ ⁢ Λ hl ← Λ hl ⁡ [ A ′ ⁢ Ξ T ⁢ V ] hl L [ α ] = ∑ j = 1 maximize N ⁢ ⁢ [ log ⁢ ⁢ Γ ⁡ ( ∑ l = 1 K ⁢ ⁢ α l ) - ∑ l = 1 K ⁢ ⁢ log ⁢ ⁢ Γ ⁡ ( α l ) + ∑ l = 1 K ⁢ ⁢ ( α l - 1 ) ⁢ ( Ψ ⁡ ( γ jl ) - Ψ ⁡ ( ∑ t = 1 K ⁢ ⁢ γ jt ) ) ] ⁢ ⁢ to ⁢ ⁢ obtain ⁢ ⁢ dirichlet ⁢ ⁢ parameter ⁢ ⁢ α .