TNERec: Topic-aware network embedding for scientific collaborator recommendation

Collaboration is increasingly becoming a vital factor in an academic network, which can bring lots of benefits for scholars. Ubiquitous intelligence also provides an effective way for scholars to find collaborators. However, due to the large-scale of scholarly big data, there is a lot of information hard to capture in networks and we need to dig out valid information from collaboration networks. It is a valuable and urgent task to find appropriate collaborators for scholars. To address these problems, we hypothesize that fusing topic model and structure information could improve the performance of recommendations. In this paper, we propose a collaborator recommendation system, named TNERec (Topic-aware Network Embedding for scientific collaborator Recommendation), learning representations from scholars' research interests and network structure. TNERec first extracts scholars' research interests based on topic model and then learns vectors of scholars with network embedding. Finally, top-k recommendation list is generated based on the scholar vectors. Experimental results on a real-world dataset show the effectiveness of the proposed framework compared with state-of-the-art collaboration recommendation baselines.