Path-aware multi-scale learning for heterogeneous graph neural network

Abstract

Heterogeneous Graph Neural Networks (HGNNs) are a powerful tool for modeling data with diverse node and edge types, found in applications like social networks, recommendation systems, and knowledge graphs, including tasks such as node classification, link prediction, and graph classification. Based on information aggregation methods, HGNNs can be broadly categorized into meta-path-free and meta-path-based HGNNs. Recently, meta-path-based HGNNs have made significant advancements in both performance and interpretability. However, these methods often overlook the redundancy among meta-paths and fail to fully leverage the inherent information within the paths, such as path length and path type. Furthermore, their insufficient utilization of global information hinders comprehensive representation learning. To address these issues, we propose a path-aware multi-scale heterogeneous graph neural network named PM-HGNN. To better capture global information, PM-HGNN employs a global similarity-based mean aggregator to pre-compute neighbor aggregation information. Additionally, PM-HGNN exploits the inherent relevance and differences between meta-paths, enabling redundancy reduction and the dynamic assignment of weights. Experiments conducted on four real-world heterogeneous graph datasets revealed that PM-HGNN consistently exceeds the performance of current state-of-the-art methods in tasks related to node classification.