1. A method for characterizing a set of documents, comprising: identifying a network of multilevel hierarchically related documents having direct and indirect references associated with content relationships; for each respective document, determining a set of latent topic characteristics captured by a Bernoulli process, based on at least both an intrinsic content of the respective document and a set of latent topic characteristics based on a respective content of other documents which are directly referenced and indirectly referenced through at least one other document to the respective document, such that a topic distribution of each respective document is a mixture of distributions associated with at least the at least one other document; representing a set of latent topics for the respective document based on a joint probability distribution of at least the latent topic characteristics based on the intrinsic content and the respective content of other documents which are directly referenced and indirectly referenced through at least one other document to the respective document, dependent on the identified network and a random process; and storing, in a memory, the represented set of latent topics for the respective document.
1. A method for characterizing a set of documents, comprising: identifying a network of multilevel hierarchically related documents having direct and indirect references associated with content relationships; for each respective document, determining a set of latent topic characteristics captured by a Bernoulli process, based on at least both an intrinsic content of the respective document and a set of latent topic characteristics based on a respective content of other documents which are directly referenced and indirectly referenced through at least one other document to the respective document, such that a topic distribution of each respective document is a mixture of distributions associated with at least the at least one other document; representing a set of latent topics for the respective document based on a joint probability distribution of at least the latent topic characteristics based on the intrinsic content and the respective content of other documents which are directly referenced and indirectly referenced through at least one other document to the respective document, dependent on the identified network and a random process; and storing, in a memory, the represented set of latent topics for the respective document. 4. The method according to claim 1 , wherein the represented set of latent topics is modeled at both a document level and a reference level, by differentiating the two different levels and the multilevel hierarchical network which is captured by the Bernoulli process.