Scientific Contribution Learning for Expertise Ranking

Expert finding in bibliographic networks has received in-creased interests in recent years. This task concerns with finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose a discriminative method to realize leading authors contributing in a scientific publication. Specifically, we cast the problem of expert find-ing in the bibliographic network to find leading experts in a research group, which is easier to solve. According to some observations, we recognize three feature groups that can dis-criminate relevant and irrelevant experts. Experimental re-sults show that the proposed method significantly improves the state-of-the-art model of expert finding in terms of all common Information Retrieval evaluation metrics