mlsanityData to 14 Oct 2026

Fixed-point graph convolutional networks against adversarial attacks

arXiv 2511.00083 ↗First posted 29 Oct 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 29 Oct 2025.

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25 of 42 references.

References · 25

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13 more
  1. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  2. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  3. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 167
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  7. 2021Elastic Graph Neural Networkscited by 67
  8. 2020Reliable Graph Neural Networks via Robust Aggregationcited by 43
  9. 2022EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networkscited by 26
  10. 2021How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implicationscited by 21
  11. 2023Robust Mid-Pass Filtering Graph Convolutional Networkscited by 17
  12. 2023Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysiscited by 14
  13. 2023Efficient Low-Rank GNN Defense Against Structural Attackscited by 2

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