Weighted Graph Structure Learning with Attention Denoising for Node Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913765294866432 |
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| author | Wang, Tingting Su, Jiaxin Liu, Haobing Jiang, Ruobing |
| author_facet | Wang, Tingting Su, Jiaxin Liu, Haobing Jiang, Ruobing |
| contents | Node classification in graphs aims to predict the categories of unlabeled nodes by utilizing a small set of labeled nodes. However, weighted graphs often contain noisy edges and anomalous edge weights, which can distort fine-grained relationships between nodes and hinder accurate classification. We propose the Edge Weight-aware Graph Structure Learning (EWGSL) method, which combines weight learning and graph structure learning to address these issues. EWGSL improves node classification by redefining attention coefficients in graph attention networks to incorporate node features and edge weights. It also applies graph structure learning to sparsify attention coefficients and uses a modified InfoNCE loss function to enhance performance by adapting to denoised graph weights. Extensive experimental results show that EWGSL has an average Micro-F1 improvement of 17.8% compared with the best baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Weighted Graph Structure Learning with Attention Denoising for Node Classification Wang, Tingting Su, Jiaxin Liu, Haobing Jiang, Ruobing Machine Learning Artificial Intelligence Node classification in graphs aims to predict the categories of unlabeled nodes by utilizing a small set of labeled nodes. However, weighted graphs often contain noisy edges and anomalous edge weights, which can distort fine-grained relationships between nodes and hinder accurate classification. We propose the Edge Weight-aware Graph Structure Learning (EWGSL) method, which combines weight learning and graph structure learning to address these issues. EWGSL improves node classification by redefining attention coefficients in graph attention networks to incorporate node features and edge weights. It also applies graph structure learning to sparsify attention coefficients and uses a modified InfoNCE loss function to enhance performance by adapting to denoised graph weights. Extensive experimental results show that EWGSL has an average Micro-F1 improvement of 17.8% compared with the best baseline. |
| title | Weighted Graph Structure Learning with Attention Denoising for Node Classification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.12157 |