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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.01207 |
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| _version_ | 1866917182906040320 |
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| author | Choi, Yoonhyuk Choi, Jiho Kim, Chanran Lee, Yumin Shin, Hawon Jeon, Yeowon Kim, Minjeong Kang, Jiwoo |
| author_facet | Choi, Yoonhyuk Choi, Jiho Kim, Chanran Lee, Yumin Shin, Hawon Jeon, Yeowon Kim, Minjeong Kang, Jiwoo |
| contents | Semi-supervised learning on real-world graphs is frequently challenged by heterophily, where the observed graph is unreliable or label-disassortative. Many existing graph neural networks either rely on a fixed adjacency structure or attempt to handle structural noise through regularization. In this work, we explicitly capture structural uncertainty by modeling a posterior distribution over signed adjacency matrices, allowing each edge to be positive, negative, or absent. We propose a sparse signed message passing network that is naturally robust to edge noise and heterophily, which can be interpreted from a Bayesian perspective. By combining (i) posterior marginalization over signed graph structures with (ii) sparse signed message aggregation, our approach offers a principled way to handle both edge noise and heterophily. Experimental results demonstrate that our method outperforms strong baseline models on heterophilic benchmarks under both synthetic and real-world structural noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01207 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sparse Bayesian Message Passing under Structural Uncertainty Choi, Yoonhyuk Choi, Jiho Kim, Chanran Lee, Yumin Shin, Hawon Jeon, Yeowon Kim, Minjeong Kang, Jiwoo Machine Learning Semi-supervised learning on real-world graphs is frequently challenged by heterophily, where the observed graph is unreliable or label-disassortative. Many existing graph neural networks either rely on a fixed adjacency structure or attempt to handle structural noise through regularization. In this work, we explicitly capture structural uncertainty by modeling a posterior distribution over signed adjacency matrices, allowing each edge to be positive, negative, or absent. We propose a sparse signed message passing network that is naturally robust to edge noise and heterophily, which can be interpreted from a Bayesian perspective. By combining (i) posterior marginalization over signed graph structures with (ii) sparse signed message aggregation, our approach offers a principled way to handle both edge noise and heterophily. Experimental results demonstrate that our method outperforms strong baseline models on heterophilic benchmarks under both synthetic and real-world structural noise. |
| title | Sparse Bayesian Message Passing under Structural Uncertainty |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.01207 |