Semantic Structure Encoding Enhancement for Heterogeneous Graph Neural Networks

Heterogeneous Graph Neural Networks (HGNNs), as an advanced method for representation learning on structured data containing multiple types of nodes and edges, have attracted widespread attention. To address the issue of performance degradation with increasing network depth, researchers have introduced the concept of metapaths. However, existing metapath-based methods fall short in capturing neighbor information of different types of nodes, leading to information loss. The selection of metapaths usually relies on domain knowledge and is not easily generalized to unseen graph structures or different tasks. In light of this, this paper proposes an innovative framework-Semantic Structure Encoding Enhanced Heterogeneous Graph Neural Network (SSEH-GNN), which efficiently captures and integrates complex structural and semantic information in the graph through the effective fusion of metapath semantic path information, achieving accurate representation of heterogeneous graph data with a certain degree of generality. To verify the effectiveness of the proposed framework, experiments on node classification tasks were conducted on multiple real-world heterogeneous graph datasets. The experimental results show that SSEH-GNN demonstrates higher predictive accuracy in node classification tasks compared to existing advanced technologies, providing a new methodology and perspective for heterogeneous graph representation learning.