Co-authorship Prediction Based on Temporal Graph Attention

Abstract

The social network analysis has received significant interests and concerns of researchers recently, and co-authorship prediction is an important link prediction problem. Traditional models inefficiently use multi-relational information to enhance topological features. In this paper, we focus on the co-authorship prediction in the co-authorship knowledge graph (KGs) to show that multi-relation graphs can enhance feature expression ability and improve prediction performance. Currently, the main models for link prediction in KGs are based on KG embedding learning, such as several models using convolutional neural networks and graph neural networks. These models capture rich and expressive embeddings of entities and relations, and obtain good results. However, the co-authorship KGs have much temporal information in reality, which cannot be integrated by these models since they are aimed at static KGs. Therefore, we propose a temporal graph attention network to model the temporal interactions between the neighbors and encapsulate the spatiotemporal context information of the entities. In addition, we also capture the semantic information and multi-hop neighborhood information of the entities to enrich the expression ability of the embeddings. Finally, our experimental evaluations on all dataset verify the effectiveness of our approach based on temporal graph attention mechanism, which outperforms the state-of-the-art models.