AHEG : attention-based heterogeneous graph convolutional networks

Graph Convolutional Networks (GCNs) are network architectures that operate on graph data. Existing GCNs often assume homogeneous graphs which cannot capture the rich semantics of the data, leading to unsatisfactory performance. Many datasets can be more naturally modeled as heterogeneous graphs which reflect intuitively and explicitly the rich semantical information between nodes. There has been little work on designing a GCN on such graph. We propose AHEG, an Attention-Based HEterogeneous Graph Convolutional Network. As compared with previous work, AHEG retrieves multiple kinds of relationships between different nodes with an efficient meta-path generation mechanism. Furthermore, with a two-stage…