Recommending funding collaborators with scholar social networks

Applying for research funding projects is becoming one of the most important ways for scientists to carry on the research. How to find an appropriate collaborator/applicant is a major concern for scientists. Social networks provide one means of visualizing existing and potential collaborations. In this paper, we study the funding collaborators recommendation problems. We solve the problem by starting with analyzing the researchers´ motivations for finding collaboration, which are (i) to form a competitive team (ii) to expand cooperation circle, which little work noticed. We model the funding relation as a complex network called co-applicant network. Based on that, we propose a utility function to take all the aspects of recommendation into account. And we propose a novel recommendation algorithm by modeling the utility function based on the group relations in the co-applicant network. We experiment our approaches on National Science Foundation of China (NSFC) funding projects and achieve effective results.