HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning
This work addresses the challenge of effectively modeling heterogeneous graphs for tasks like node classification and link prediction, representing an incremental improvement over existing methods.
The authors tackled the problem of learning representations from heterogeneous graphs by proposing HMSG, a model that decomposes the graph into metapath-based subgraphs to capture structural, semantic, and attribute information from both homogeneous and heterogeneous neighbors, achieving the best performance in node classification, clustering, and link prediction tasks across multiple datasets.
Many real-world data can be represented as heterogeneous graphs with different types of nodes and connections. Heterogeneous graph neural network model aims to embed nodes or subgraphs into low-dimensional vector space for various downstream tasks such as node classification, link prediction, etc. Although several models were proposed recently, they either only aggregate information from the same type of neighbors, or just indiscriminately treat homogeneous and heterogeneous neighbors in the same way. Based on these observations, we propose a new heterogeneous graph neural network model named HMSG to comprehensively capture structural, semantic and attribute information from both homogeneous and heterogeneous neighbors. Specifically, we first decompose the heterogeneous graph into multiple metapath-based homogeneous and heterogeneous subgraphs, and each subgraph associates specific semantic and structural information. Then message aggregation methods are applied to each subgraph independently, so that information can be learned in a more targeted and efficient manner. Through a type-specific attribute transformation, node attributes can also be transferred among different types of nodes. Finally, we fuse information from subgraphs together to get the complete representation. Extensive experiments on several datasets for node classification, node clustering and link prediction tasks show that HMSG achieves the best performance in all evaluation metrics than state-of-the-art baselines.