As most great discoveries and advancements in science and technology invariably involve the cooperation of a group of researchers, effective collaboration is the key factor. Nevertheless, finding suitable scholars and researchers to work with is challenging and, mostly, time-consuming for many. A recommender who is capable of finding and recommending collaborators would prove helpful. In this work, we utilized a life science and biomedical research database, i.e., MEDLINE, to develop a collaboration recommendation system based on novel graph neural networks, i.e., GraphSAGE and Temporal Graph Network, which can capture intrinsic, complex, and changing dependencies among researchers, including temporal user-user interactions. The baseline methods based on LightGCN and gradient boosting trees were also developed in this work for comparison. Internal automatic evaluations and external evaluations through end-users' ratings were conducted, and the results revealed that our graph neural networks recommender exhibits consistently encouraging results.
Keywords: artificial intelligence; collaborator recommendation; deep learning; graph neural networks (GNN); recommendation systems.
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