1. A method for processing a corpus of documents having a multi-level transitive linkage structure with other documents, comprising: providing a computer-implemented generative model based on at least parameters estimated by probabilistic inference, which models a content of each document in the document database based on at least (i) an intrinsic content of each respective document; and (ii) a content of related documents linked to each respective document through the multi-level transitive linkage structure, the computer-implemented generative model representing the content of each respective document as at least a mixture over latent topics having topic distributions which are a mixture of distributions associated with the related documents comprising at least a mixture weighting of the intrinsic content of each respective document and the content of related documents linked to each respective document through the multi-level transitive linkage structure; at least one of representing, characterizing, clustering, summarizing, indexing, ranking, and searching the documents in the corpus of documents with at least one automated processor based on at least the computer-implemented generative model; and controlling a human machine interface of an information processing system to receive a user input, and selectively dependent on the user input and the at least one of representing, characterizing, clustering, summarizing, indexing, ranking, and searching of the documents in the corpus of documents, to output a representation of a relationship of a plurality of respective documents.
1. A method for processing a corpus of documents having a multi-level transitive linkage structure with other documents, comprising: providing a computer-implemented generative model based on at least parameters estimated by probabilistic inference, which models a content of each document in the document database based on at least (i) an intrinsic content of each respective document; and (ii) a content of related documents linked to each respective document through the multi-level transitive linkage structure, the computer-implemented generative model representing the content of each respective document as at least a mixture over latent topics having topic distributions which are a mixture of distributions associated with the related documents comprising at least a mixture weighting of the intrinsic content of each respective document and the content of related documents linked to each respective document through the multi-level transitive linkage structure; at least one of representing, characterizing, clustering, summarizing, indexing, ranking, and searching the documents in the corpus of documents with at least one automated processor based on at least the computer-implemented generative model; and controlling a human machine interface of an information processing system to receive a user input, and selectively dependent on the user input and the at least one of representing, characterizing, clustering, summarizing, indexing, ranking, and searching of the documents in the corpus of documents, to output a representation of a relationship of a plurality of respective documents. 5. The method according to claim 1 , wherein the generative model is a Bernoulli Process Topic model.