10. A method for characterizing a set of documents, comprising: identifying a multilevel hierarchy of documents having direct references and indirect references, having citation linkages with semantic significance; for each respective document, determining latent topic characteristics captured by a Bernoulli process, based on at least an intrinsic semantic content of the respective document, semantic content associated with directly cited documents, and semantic content associated with documents referenced by directly cited documents, wherein a semantic content significance of a citation has a transitive property, such that a topic distribution of each respective document is a mixture of distributions associated with at least the cited documents; representing latent topics for documents within the set of documents based on a joint probability distribution of the latent topic characteristics, wherein distinctions in the multilevel hierarchical network are captured by a random process; and storing, in a memory, the represented set of latent topics.
10. A method for characterizing a set of documents, comprising: identifying a multilevel hierarchy of documents having direct references and indirect references, having citation linkages with semantic significance; for each respective document, determining latent topic characteristics captured by a Bernoulli process, based on at least an intrinsic semantic content of the respective document, semantic content associated with directly cited documents, and semantic content associated with documents referenced by directly cited documents, wherein a semantic content significance of a citation has a transitive property, such that a topic distribution of each respective document is a mixture of distributions associated with at least the cited documents; representing latent topics for documents within the set of documents based on a joint probability distribution of the latent topic characteristics, wherein distinctions in the multilevel hierarchical network are captured by a random process; and storing, in a memory, the represented set of latent topics. 13. The method according to claim 10 , wherein the joint probability distribution is estimated by an iterative process at a citation level, comprising, for each document d j , and for the i-th location in document d j , choosing a topic z ji from the topic distribution of document d j , p(z|d j ,θ d j ), where the distribution parameter θ d j is drawn from a Dirichlet distribution Dir(α), choosing a word w ji which follows the multinomial distribution p(w|z ji ,Λ) conditioned on the topic z ji , and respectively incrementing the locations and documents, wherein the joint probability distribution is estimated by an iterative process at a document level comprising, for each document d s , and for the i-th location in document d s , choosing a cited document c si from p(c|d s ,Ξ), a multinomial distribution conditioned on the document d s choosing a topic t si from the topic distribution of the document c si at the citation level, and choosing a word w si which follows the multinomial distribution p(w|t si ,Λ) conditioned on the topic t si , where Ξ is a mixing coefficient matrix which represents how much of the content of the respective document is from direct or indirect references, and a composition of Ξ and θ models the topic distribution at the document level, and respectively incrementing the locations and documents, wherein a number of latent topics is K and the mixing coefficients are parameterized by an N×N matrix Ξ, where Ξ js =p(c si =d j |d s ), which are treated as a fixed quantity computed from the citation information of the set of documents, wherein topic distributions at the citation level are parameterized by a K×N matrix Θ where Θ lj =p(z ji =l|d j ), which is to be estimated, and an M×K word probability matrix Λ, where Λ hl =p(w si h =1|t si =l), which is to be estimated, and wherein each document d s has a set of citations Q d s , further comprising constructing a matrix S to denote direct relationships among the documents wherein S ls =1/|Q d s | for d l εQ d s and 0 otherwise, where |Q d s | denotes the size of the set Q d s , and employing a generative process for generating a related document c from the respective document d s , comprising: setting l=s; choosing t˜Bernoulli(β); if t=1, choosing h˜Multinomial(S .,l ), where S .,l denotes the l-th column; setting l=h, and returning to said choosing step; and if t=0, letting c=d l , to thereby combine a Bernoulli process and a random walk on a directed graph together, where a transitive property of the citations is captured, wherein the parameter β of the Bernoulli process determines a probability that the random walk stops at a current node, and the parameter β also specifies how much of the content of the respective document is influenced from the direct or indirect citations.