Construction and Application of Heterogeneous Graph Neural Network Model Fusing Meta-Path Sequence Features

In real-world applications, the prevalence of heterogeneous graph data has driven the development of heterogeneous graph neural networks (HGNNs) as an effective solution for modeling intricate semantic relationships. A widely adopted strategy involves using meta-paths as high-level structural motifs to direct neighborhood aggregation in HGNNs. Nevertheless, the semantic content inherent in meta-paths themselves is often not fully exploited, even though they are typically employed as guiding signals. This paper introduces a new HGNN architecture that utilizes meta-path sequences, integrating the intrinsic information of meta-paths directly into the semantic fusion mechanism. By representing meta-paths as sequential data—similar to sequences in natural language processing—we are able to capture more detailed semantic patterns through the sequential order of node types in heterogeneous graphs. Using sequence modeling methods, our approach embeds meta-path semantics into the graph neural network, offering not only additional structural insights but also enabling the training of specialized embeddings for node types. We perform extensive experiments, comprising comparative and ablation analyses, on a custom-built dataset and three publicly available medium-scale heterogeneous graph benchmarks. The experimental outcomes validate the efficacy of our method in utilizing sequential characteristics of meta-paths to improve representation learning.