Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910714822656000 |
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| author | Wang, Yongyu Zhuang, Xiaotian |
| author_facet | Wang, Yongyu Zhuang, Xiaotian |
| contents | Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are often affected by redundant and erroneous edges present within the constructed graphs. In this paper, we view these noisy edges as adversarial attack and propose to use a spectral adversarial robustness evaluation method to mitigate the impact of noisy edges on the performance of graph-based algorithms. Our method identifies the points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms. Our experiments demonstrate that our methodology is highly effective and outperforms state-of-the-art denoising methods by a large margin. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15615 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation Wang, Yongyu Zhuang, Xiaotian Machine Learning Cryptography and Security Computer Vision and Pattern Recognition Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are often affected by redundant and erroneous edges present within the constructed graphs. In this paper, we view these noisy edges as adversarial attack and propose to use a spectral adversarial robustness evaluation method to mitigate the impact of noisy edges on the performance of graph-based algorithms. Our method identifies the points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms. Our experiments demonstrate that our methodology is highly effective and outperforms state-of-the-art denoising methods by a large margin. |
| title | Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation |
| topic | Machine Learning Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.15615 |