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

Yongyu Wang, Xiaotian Zhuang

TL;DR

This paper proposes to use a spectral adversarial robustness evaluation method to mitigate the impact of noisy edges on the performance of graph-based algorithms.

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.

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

TL;DR

This paper proposes to use a spectral adversarial robustness evaluation method to mitigate the impact of noisy edges on the performance of graph-based algorithms.

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.
Paper Structure (17 sections, 1 equation, 1 figure, 2 tables, 1 algorithm)

This paper contains 17 sections, 1 equation, 1 figure, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Adversarial attack and defense
  4. Spectral clustering
  5. Method
  6. Examining Graph-Based Machine Learning Models from the Perspective of Adversarial Attacks
  7. A Proactive Defense Strategy from the Node Perspective to Mitigate the Impact of Noisy Edges
  8. Adversarial Robustness Evaluation
  9. A Multi-Level Algorithm Framework Based on Robust Node Set
  10. Experiment
  11. Data sets
  12. Metric
  13. Compared Algorithms
  14. Results Of Solution Quality
  15. Efficacy of Resolving Computational Bottleneck
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Eigen-decomposition time of original node set and robust node set.