KAGNN: Graph neural network with kernel alignment for heterogeneous graph learning

Abstract

Current studies have proposed the incorporation of kernel methods with graph representation learning, and graph kernels have attracted widespread attention for assessing graph similarity. However, owing to graph heterogeneity, developing appropriate kernels to extract complex structures and semantics from heterogeneous graphs is challenging. In this study, we developed a kernel-based heterogeneous graph neural network (GNN) model with a novel graph label kernel and an alignment-based aggregation mechanism. The graph label kernel captures the heterogeneous characteristics and integrates them with the graph aggregation mechanism into a unified framework. We employed the kernel alignment mechanism in a GNN framework to update node representation. The developed alignment-based GNN framework can automatically process feature propagation by modeling the similarity between node pairs and aligning the similarity matrix with the label kernel. Furthermore, model training avoids complex aggregation processes and captures distinguishable features during kernel alignment. Finally, we conducted a theoretical analysis to demonstrate the effectiveness of our method. Considering practicality, we optimized the implementation to reduce the running costs. We tested our method on three typical datasets, and the results showed that it outperformed state-of-the-art baselines.