THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation
This addresses limitations in HGNNs for modeling heterogeneous information networks, offering improved node classification with reduced computational overhead.
The paper tackles the problem of type information loss and structural noise in Heterogeneous Graph Neural Networks (HGNNs) by proposing THeGAU, a model-agnostic framework that combines a type-aware graph autoencoder with guided graph augmentation, which consistently outperforms existing methods on benchmark datasets like IMDB, ACM, and DBLP.
Heterogeneous Graph Neural Networks (HGNNs) are effective for modeling Heterogeneous Information Networks (HINs), which encode complex multi-typed entities and relations. However, HGNNs often suffer from type information loss and structural noise, limiting their representational fidelity and generalization. We propose THeGAU, a model-agnostic framework that combines a type-aware graph autoencoder with guided graph augmentation to improve node classification. THeGAU reconstructs schema-valid edges as an auxiliary task to preserve node-type semantics and introduces a decoder-driven augmentation mechanism to selectively refine noisy structures. This joint design enhances robustness, accuracy, and efficiency while significantly reducing computational overhead. Extensive experiments on three benchmark HIN datasets (IMDB, ACM, and DBLP) demonstrate that THeGAU consistently outperforms existing HGNN methods, achieving state-of-the-art performance across multiple backbones.