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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.05540 |
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| _version_ | 1866909678929182720 |
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| author | Gu, Chunhui Nasr, Mohammad Sadegh Long, James P. Do, Kim-Anh Irajizad, Ehsan |
| author_facet | Gu, Chunhui Nasr, Mohammad Sadegh Long, James P. Do, Kim-Anh Irajizad, Ehsan |
| contents | Graph Neural Networks (GNNs) often struggle with noisy edges. We propose Latent Space Constrained Graph Neural Networks (LSC-GNN) to incorporate external "clean" links and guide embeddings of a noisy target graph. We train two encoders--one on the full graph (target plus external edges) and another on a regularization graph excluding the target's potentially noisy links--then penalize discrepancies between their latent representations. This constraint steers the model away from overfitting spurious edges. Experiments on benchmark datasets show LSC-GNN outperforms standard and noise-resilient GNNs in graphs subjected to moderate noise. We extend LSC-GNN to heterogeneous graphs and validate it on a small protein-metabolite network, where metabolite-protein interactions reduce noise in protein co-occurrence data. Our results highlight LSC-GNN's potential to boost predictive performance and interpretability in settings with noisy relational structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05540 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Gu, Chunhui Nasr, Mohammad Sadegh Long, James P. Do, Kim-Anh Irajizad, Ehsan Machine Learning Artificial Intelligence Graph Neural Networks (GNNs) often struggle with noisy edges. We propose Latent Space Constrained Graph Neural Networks (LSC-GNN) to incorporate external "clean" links and guide embeddings of a noisy target graph. We train two encoders--one on the full graph (target plus external edges) and another on a regularization graph excluding the target's potentially noisy links--then penalize discrepancies between their latent representations. This constraint steers the model away from overfitting spurious edges. Experiments on benchmark datasets show LSC-GNN outperforms standard and noise-resilient GNNs in graphs subjected to moderate noise. We extend LSC-GNN to heterogeneous graphs and validate it on a small protein-metabolite network, where metabolite-protein interactions reduce noise in protein co-occurrence data. Our results highlight LSC-GNN's potential to boost predictive performance and interpretability in settings with noisy relational structures. |
| title | Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.05540 |