A Semantic Subgraphs Based Link Prediction Method for Heterogeneous Social Networks with Graph Attention Networks

摘要

Link prediction is a very important research issue in social networks analysis, and it has a very wide range of applications. Real world social networks are usually heterogeneous networks which contain rich semantic information. Meta-paths are often used to characterize this semantic information in the analysis of heterogeneous social networks. Existing methods either use only topology information or use only a single meta path to extract semantic information in the network. In this paper, we propose a link prediction method based on SEmantic Subgraphs and Graph ATtention network (SESGAT). SESGAT not only makes full use of the different semantic information contained in different semantic subgraphs, but also uses the attention mechanism to learn the different importance of different semantic subgraphs for link prediction. Experiment results on real social networks show that our approach exhibits better predictive performance than other state-of-the-art methods.