Unsupervised prediction of citation influences

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

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Laura Dietz, Steffen Bickel, Tobias Scheffer
• Keywords: ICML 2007, Machine Learning, Probabilistic Topic Model, Scientific Research Areas, Topical Inheritance