Abstract

Expert finding problem in bibliographic networks has received increased interests in recent years. This problem 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 two discriminative methods to realize leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. We recognize three feature groups that can discriminate relevant experts from other authors of a document. Experimental results on a real dataset, and a synthetic one that is gathered from Microsoft academic search engine show that the proposed model significantly improves the performance of expert finding in terms of all common Information Retrieval evaluation metrics.