Unsupervised prediction of citation influences

Abstract Publication repositories contain an abundance of information about the of scientific research areas. We address the problem of creating a of a research area that describes the flow of topics between, quantifies the impact that papers have on each other, and helps to key contributions. To this end, we devise a probabilistic topic model explains the generation of documents; the model incorporates the aspects topical innovation and topical inheritance via citations. We evaluate the 's ability to predict the strength of influence of citations against rated citations.