THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918244263133184 |
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| author | Hong, Ming-Yi Chiang, Miao-Chen Teng, Youchen Wang, Yu-Hsiang Wang, Chih-Yu Lin, Che |
| author_facet | Hong, Ming-Yi Chiang, Miao-Chen Teng, Youchen Wang, Yu-Hsiang Wang, Chih-Yu Lin, Che |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10589 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation Hong, Ming-Yi Chiang, Miao-Chen Teng, Youchen Wang, Yu-Hsiang Wang, Chih-Yu Lin, Che Machine Learning 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. |
| title | THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.10589 |