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Many real-world data can be represented as heterogeneous graphs withdifferent types of nodes and connections. Heterogeneous graph neural networkmodel aims to embed nodes or subgraphs into low-dimensional vector space forvarious downstream tasks such as node classification, link prediction, etc.Although several models were proposed recently, they either only aggregateinformation from the same type of neighbors, or just indiscriminately treathomogeneous and heterogeneous neighbors in the same way. Based on theseobservations, we propose a new heterogeneous graph neural network model namedHMSG to comprehensively capture structural, semantic and attribute informationfrom both homogeneous and heterogeneous neighbors. Specifically, we firstdecompose the heterogeneous graph into multiple metapath-based homogeneous andheterogeneous subgraphs, and each subgraph associates specific semantic andstructural information. Then message aggregation methods are applied to eachsubgraph independently, so that information can be learned in a more targetedand efficient manner. Through a type-specific attribute transformation, nodeattributes can also be transferred among different types of nodes. Finally, wefuse information from subgraphs together to get the complete representation.Extensive experiments on several datasets for node classification, nodeclustering and link prediction tasks show that HMSG achieves the bestperformance in all evaluation metrics than state-of-the-art baselines.

HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning