MIGTNet: Metapath Instance-based Graph Transformation Network for heterogeneous graph embedding

Many real-world graphs are heterogeneous with various types of entities and relations. These heterogeneous graphs commonly inherent various structural and semantic information of sequences of node types, called metapaths; and many current heterogeneous graph embedding models learn not only the node features but also the metapaths. However, all of them only focus on the metapath instances (i.e., the sequences of nodes following the schema defined by a metapath) and ignore the relations between them when learning about the metapaths. To overcome this limitation, we propose a new model called the Metapath Instance-based Graph Transformation Network (MIGTNet) for heterogeneous graph embedding, which uses both metapath instances and relations between them. MIGTNet constructs a metapath instance-based graph, where a node represents a metapath instance and a link represents a relation between metapath instances, and inputs it to a hierarchical graph attention network to obtain meaningful node embeddings. Extensive experiments using real datasets show that MIGTNet outperforms state-of-the-art heterogeneous graph embedding models in node classification and node clustering.