Link prediction for heterogeneous information networks based on enhanced meta-path aggregation and attention mechanism

Heterogeneous link prediction aims to reveal potential connections between two nodes in heterogeneous information networks. Most existing studies are based on meta-paths, but ignore the information contained in incomplete meta-paths. They simply aggregate meta-paths, leading to mining semantic information insufficiently. To solve this problem, we propose a link prediction model based on enhanced meta-path aggregation and attention mechanism. In this model, the deficiency of missing topological information from incomplete meta-paths is compensated by aggregating structural features and semantics. Different from existing meta-path encoders, we use recurrent neural networks and the attention mechanism to learn explicit and implicit semantic knowledge from meta-paths, which can capture more complex semantic associations between nodes. In addition, to avoid duplicate feature acquisition by random walking, we design a novel bidirectional biased random walking algorithm. It is applied to guide the generation of heterogeneous neighbors of each node that contain features ignored by the meta-path-wise model, which can mine complete topological information and get more accurate link prediction results. The extensive experiments on several datasets demonstrate that the proposed model outperforms baselines.