Graph Embedding for Scholar Recommendation in Academic Social Networks - Yuan, Chengzhe; He, Yi; Lin, Ronghua; Tang, Yong (Frontiers in Physics, 2021)
The academic social networks (ASNs) play an important role in promoting scientific collaboration and innovation in academic society. Accompanying the tremendous growth of scholarly big data, finding suitable scholars on ASNs for collaboration has been ever difficult. Different from friend recommendation in conventional social networks, scholar recommendation in ASNs usually involves different academic entities (e.g., scholars, scientific publications and status updates) and various relationships (e.g., collaboration relationship between team members, citations and co-authorships), which forms a complex heterogeneous academic network. In this paper, we propose to design a scholar recommendation system by leveraging academic auxiliary information and a graph embedding framework. First, we construct an enhanced ASNs by integrating two types of academic features extracted from scholars’ academic information with original network topology. Then, the refined feature representations of the scholars are obtained by graph embedding framework, which helps the system to generate personalized recommendations for each individual scholar. We evaluate the effectiveness of our model on three real-world datasets: SCHOLAT, Zhihu and APS. The experimental results demonstrate that our model is effective and achieves promising improvements than the other competitive baselines.