Related papers: Addressing Noise and Efficiency Issues in Graph-Based Machine Learning Models From the Perspective of Adversarial Attack

Addressing Noise and Efficiency Issues in Graph-Based Machine Learning Models From the Perspective of Adversarial Attack

URL: http://arxiv.org/abs/2401.15615v1
Date: Sun, 28 Jan 2024 10:03:37 GMT
Title: Addressing Noise and Efficiency Issues in Graph-Based Machine Learning Models From the Perspective of Adversarial Attack
Authors: Yongyu Wang
Abstract summary: We propose treating noisy edges as adversarial attack and use a spectral adversarial robustness evaluation method to diminish the impact of noisy edges on the performance of graph algorithms. Our method identifies those points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms.
Score: 2.1937382384136637
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are invariably affected by the plethora of redundant and erroneous edges present within the constructed graphs. In this paper, we propose treating these noisy edges as adversarial attack and use a spectral adversarial robustness evaluation method to diminish the impact of noisy edges on the performance of graph algorithms. Our method identifies those points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms. Our experiments with spectral clustering, one of the most representative and widely utilized graph algorithms, reveal that our methodology not only substantially elevates the precision of the algorithm but also greatly accelerates its computational efficiency by leveraging only a select number of robust data points.

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