Forming a research team of experts in expert-skill co-occurrence network of research news

摘要

The team formation problem is required to find a group of individuals that can match the skills required by a collaborative task. Large-scale and comprehensive scientific research tasks need skilled experts from various fields to form a research team and work for it. This paper constructs a dataset and proposes team formation algorithms to find out research teams, which provides decision support for the research projects. The size of existing datasets is relatively small and fields of experts in it are less diversified. This paper extracts information of experts and skills from research news to construct a co-occurrence network with heterogeneous network structure. Based on the dataset, this work designs approximate algorithms regarding skill as the priority to find near optimum teams with provable guarantees. On heterogeneous structure, the proposed algorithms directly search requested skills to form the subgraph of team, which achieve significant improvement in time efficiency. Experimental results suggest that our methods can form the high-quality research team, and have better efficiently compared to naive strategies and scale well with the size of the data.