Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation

Yongyu Wang; Xiaotian Zhuang

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arXiv · 2401.15615

Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation

Yongyu Wang·Xiaotian Zhuang

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Abstract

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.

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Yongyu Wang, Xiaotian Zhuang. 2024-01-28. Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation. https://arxiv.org/abs/2401.15615

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