Meta-Path Generation Online for Heterogeneous Network Embedding

Abstract

Graph neural networks (GNNs), powerful deep representation learning methods for graph data, have been widely used in various tasks, such as recommendation systems and link prediction. Most existing GNNs are designed to learn node embeddings on homogeneous graphs. Heterogeneous information network (HIN) with various types of nodes and edges still faces great challenges for the heterogeneity and rich semantic information. To make full use of the heterogeneous information, many works try to manually design meta-paths, which are paths connected with two objects. They utilize meta-paths to capture more semantic information in heterogeneous graphs. However, manually designed meta-paths require domain knowledge and meta-path-based heterogeneous graph embedding methods only utilize the information of nodes with the same type, ignoring the impacts of the different types of nodes. We propose meta-path generation online for heterogeneous network embedding for all types of nodes, which can generate meta-paths and learn node embeddings simultaneously. Firstly, we exhaust all meta-paths within k-hop for specific nodes and apply a meta-path guided nodes aggregation. Secondly, we adopt an attention mechanism to select Top-N meta-paths with the largest attention coefficients for the semantic aggregation. The above two stages constitute one layer of our approach. Through stacking multi-layers, we can generate longer and more complex meta-paths. Without domain-specific preprocessing, extensive experiments on two datasets demonstrate that our proposed approach achieves better performance compared with other recent methods that require predefined meta-paths from domain knowledge.