HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning

TL;DR

HMSG is a novel heterogeneous graph neural network that decomposes graphs into metapath-based subgraphs to better capture diverse structural, semantic, and attribute information, leading to superior performance in various tasks.

Contribution

The paper introduces HMSG, a new GNN model that leverages metapath-based subgraphs and type-specific transformations for enhanced heterogeneous graph learning.

Findings

HMSG outperforms state-of-the-art baselines in node classification.

HMSG achieves superior results in node clustering and link prediction.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

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.