Fixed-point graph convolutional networks against adversarial attacks
This addresses the vulnerability of graph neural networks to adversarial manipulation, which is crucial for applications relying on graph data integrity, though it appears incremental as it builds on existing defense mechanisms with a novel filtering approach.
The paper tackles the problem of adversarial attacks on graph neural networks by proposing a fixed-point iterative graph convolutional network (Fix-GCN) that achieves robustness by capturing higher-order neighborhood information without extra computational cost, demonstrating effectiveness through experiments on benchmark datasets.
Adversarial attacks present a significant risk to the integrity and performance of graph neural networks, particularly in tasks where graph structure and node features are vulnerable to manipulation. In this paper, we present a novel model, called fixed-point iterative graph convolutional network (Fix-GCN), which achieves robustness against adversarial perturbations by effectively capturing higher-order node neighborhood information in the graph without additional memory or computational complexity. Specifically, we introduce a versatile spectral modulation filter and derive the feature propagation rule of our model using fixed-point iteration. Unlike traditional defense mechanisms that rely on additional design elements to counteract attacks, the proposed graph filter provides a flexible-pass filtering approach, allowing it to selectively attenuate high-frequency components while preserving low-frequency structural information in the graph signal. By iteratively updating node representations, our model offers a flexible and efficient framework for preserving essential graph information while mitigating the impact of adversarial manipulation. We demonstrate the effectiveness of the proposed model through extensive experiments on various benchmark graph datasets, showcasing its resilience against adversarial attacks.