Mining a digital library for influential authors

David M. Mimno, Andrew McCallum

Abstract: When browsing a digital library of research papers, it is natural to ask which authors are most influential in a particular topic. We present a probabilistic model that ranks authors based on their influence in particular areas of scientific research. This model combines several sources of information: citation information between documents as represented by PageRank scores, authorship data gathered through automatic information extraction, and the words in paper abstracts. We compare the performance of a topic model versus a smoothed language model by assessing the number of major award winners in the resulting ranked list of researchers.