mlsanityData to 7 Oct 2026

SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks

arXiv 2408.16537 ↗First posted 29 Aug 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. Yet, GNNs are often vulnerable to adversarial structural attacks as embedding generation relies on graph topology. Existing efforts are dedicated to purifying the maliciously modified structure or applying adaptive aggregation, thereby enhancing the robustness against adversarial structural attacks. It is inevitable for a defender to consume heavy computational costs due to lacking prior knowledge about modified structures. To this end, we propose an efficient defense method, called Simple and Fast Robust Graph Neural Network (SFR-GNN), supported by mutual information theory. The SFR-GNN first pre-trains a GNN model using node attributes and then fine-tunes it over the modified graph in the manner of contrastive learning, which is free of purifying modified structures and adaptive aggregation, thus achieving great efficiency gains. Consequently, SFR-GNN exhibits a 24%--162% speedup compared to advanced robust models, demonstrating superior robustness for node classification tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 29 Aug 2024.

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23 of 39 references.

References · 23

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2018How Powerful are Graph Neural Networks?cited by 4,355
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  11. 2021Robustness of Graph Neural Networks at Scalecited by 100
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11 more
  1. 2020DeepRobust: A PyTorch Library for Adversarial Attacks and Defensescited by 78
  2. 2021Bag of Tricks for Node Classification with Graph Neural Networkscited by 40
  3. 2021Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Studycited by 36
  4. 2022Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNNcited by 34
  5. 2023Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debiascited by 16
  6. 2023MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Datacited by 14
  7. 2023Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detectioncited by 12
  8. 2021Structack: Structure-based Adversarial Attacks on Graph Neural Networkscited by 8
  9. 2022FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classificationcited by 7
  10. 2022Towards Adversarially Robust Recommendation from Adaptive Fraudster Detectioncited by 3
  11. 2023Black-Box Attacks against Signed Graph Analysis via Balance Poisoningcited by 3

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