Knowledge Graph-based Embedding for Connecting Scholars in Academic Social Networks

In recent years, research tasks have increasingly involved using multi-disciplinary knowledge through collaborations of scholars from multiple fields. However, identifying a team of suitable collaborators from diverse fields for a given research task is a challenging and time-consuming process. In this paper, we propose a novel “ScholarTeamFinder” model that uses knowledge graph based link prediction to identify collaborators within an academic social network (ASN) to form a research team to address a multi-disciplinary research problem. Our approach involves building a heterogeneous knowledge graph within an ASN using entities such as scholars, publications, research grants, and the relationship among these entities. Following this, we use graph-based deep learning to learn the node embedding from the knowledge graph that can be used for scholar team recommendation. More specifically, we used the classical meth-path2vec as our base graph learning algorithm and improved its performance by considering semantic meaning of entities and encoding edge embeddings in the graph. Finally, we propose a beam-search algorithm for scholar team prediction based on our model embeddings. Our evaluation of ScholarTeamFinder is performed using large ASN datasets including a unique dataset (i.e., NSF award dataset) of federal grant awards collected over the last ten years and the scholars’ publication data, as well as three other widely used datasets (i.e., APS, SCHOLAT and Gowalla). Experiment results show that our model outperforms the state-of-the-art models across the different datasets.