Attention based Collaborator Recommendation in Heterogeneous Academic Networks

In real academic networks, there exist multiple types of entities(authors, papers, terms, conferences) and links between them. Therefore, the academic networks are generally considered as heterogeneous information networks(HINs). Existing collaborator recommendation methods in heterogeneous networks are generally based on the embeddings of nodes and links with respect to some given meta-paths. However, they seldom learn meta-paths representations which can provide important interaction information. What's more, the impact of different meta-paths on recommendation are neglected. In order to deal with these unsolved problems, we propose an attention based collaborator recommendation method in the setting of heterogeneous academic networks. Firstly, we select some meta-paths according to the HIN schema. Secondly, the embeddings of nodes and meta-path instances are generated by employing the Skip-gram and Convolutional Neural Network(CNN) models respectively. Thirdly, the attention mechanism is devised to integrate the multiple sources of embeddings so as to produce the author representations and meta-path based context representations. Finally, the Multi-Layer Perceptron is utilized for recommendation task. Comparative experiments conducted on the DBLP dataset demonstrate the effectiveness of our proposed method.