For expertise retrieval purposes we can rely on the endorsements received by members of a social network, with respect to some skill.Skills are correlated, which may affect individual rankings.An endorsement deduction method is proposed, which improves data consistency and completeness.Endorsement deduction makes use of the known correlation among skills.The method is validated in a synthetic network resembling LinkedIn. Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements. Display Omitted
Expert finding in bibliographic networks has received increased 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 finding in a 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 discriminate relevant and irrelevant experts. Experimental results on a real dataset, and an automatically generated one that is gathered from Microsoft academic search show that the proposed model significantly improves the performance of expert finding in terms of all common Information Retrieval evaluation metrics.