THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Node ClassificationSimilar Papers 제목 키워드 기반
Generative-Contrastive Heterogeneous Graph Neural Network
Heterogeneous Graphs (HGs) effectively model complex relationships in the real world through multi-type nodes and edges. In recent years, inspired by self-supervised learning (SSL), contrastive learning (CL)-based Hetero…
Contrastive LearningData AugmentationGraph Neural NetworkLink Prediction+2AEGCN: An Autoencoder-Constrained Graph Convolutional Network
We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its name, the core of this model is a convol…
DecoderGraph AttentionNode ClassificationHetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness
Heterogeneous graph neural networks(HGNNs) have recently shown impressive capability in modeling heterogeneous graphs that are ubiquitous in real-world applications. Most existing methods for heterogeneous graphs mainly …
AttributeHeterogeneous Graph Condensation via Role-Aware Clustering
Heterogeneous Graph Neural Networks (HGNNs) have exhibited remarkable efficacy in modeling complex systems with multiple types of nodes and relations, yet their training on large-scale heterogeneous graphs remains comput…
Bilevel OptimizationIVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder
Handling missing data remains a fundamental challenge in real-world tabular datasets, especially when data are heterogeneous with both numerical and categorical features. Existing imputation methods often fail to capture…
Graph Representation Learning