HNERec: Scientific collaborator recommendation model based on heterogeneous network embedding

Abstract

Scientific collaboration contributes to the innovation of science and technology. For individual scholars, it is valuable to find potential and valuable collaborators to improve the quality and quantity of research. Previous research mainly recommends collaborators by exploring one kind of relationship between authors, such as the research topics or co-authorship. Some research tries to utilize more relationships, but they simply integrate the recommendation results of different relationships rather than integrating them. This study proposes a novel Heterogeneous Network Embedding Recommendation (HNERec) model for scientific collaborator recommendation. The HNERec model employs a random walk to develop a heterogeneous network considering four kinds of meta-path illustrating the topic relationship, authorship, citation relationship, and venue relationship. It utilizes the skip-gram model to embed the nodes, and finally generates a recommendation list based on the similarity between the corresponding node vectors. Experiments on a DBLP dataset and an AMiner dataset demonstrate the reliability of the HNERec model. The HNERec model outperforms the mainstream collaborator recommendation baselines in metrics such as F1, MRR, and nDCG. The results can not only be applied to the selection of collaborators by individual scholars but also allow scientific and conference institutions to recommend collaborators.