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. 12. The method according to claim 10 , wherein the latent topics are modeled at both a document level and a citation level, and distinctions in the multilevel hierarchical network are captured by the Bernoulli process.