A Recommender for Research Collaborators Using Graph Neural Networks - Zhu, Jie; Yaseen, Ashraf (Frontiers in Artificial Intelligence, 2022)
As most great discoveries and advancements in science and technology invariably involve the cooperation of a group of researchers, effective collaboration is key. Nevertheless, finding suitable scholars and researchers to work with is challenging and, mostly, time-consuming for many. A recommender 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. Baseline methods based on LightGCN and gradient boosting trees were also developed in this work for comparison. Internal automatic evaluations, as well as external evaluations through end-users’ ratings were conducted, and results revealed that our graph neural networks recommender exhibits consistently encouraging results.