mlsanityData to 14 Oct 2026

Rethinking the Trigger-injecting Position in Graph Backdoor Attack

arXiv 2304.02277 ↗First posted 5 Apr 2023cs.LG cs.CRcited by 4

Not on the map. Why?

Abstract (arXiv)

Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such that the backdoored model will perform abnormally on inputs with predefined backdoor triggers and still retain state-of-the-art performance on the clean inputs. While there are already some works on backdoor attacks on Graph Neural Networks (GNNs), the backdoor trigger in the graph domain is mostly injected into random positions of the sample. There is no work analyzing and explaining the backdoor attack performance when injecting triggers into the most important or least important area in the sample, which we refer to as trigger-injecting strategies MIAS and LIAS, respectively. Our results show that, generally, LIAS performs better, and the differences between the LIAS and MIAS performance can be significant. Furthermore, we explain these two strategies' similar (better) attack performance through explanation techniques, which results in a further understanding of backdoor attacks in GNNs.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 5 Apr 2023.

Builds on

22 of 37 references.

References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  5. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  6. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  7. 2020Parameterized Explainer for Graph Neural Networkcited by 426
  8. 2018Deep Learning on Graphs: A Surveycited by 393
  9. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
  10. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  11. 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
  12. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
10 more
  1. 2019Explainability Techniques for Graph Convolutional Networkscited by 161
  2. 2020Higher-Order Explanations of Graph Neural Networks via Relevant Walkscited by 138
  3. 2020Backdoor Attacks to Graph Neural Networkscited by 128
  4. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  5. 2020Graph Backdoorcited by 112
  6. 2021Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibrationcited by 92
  7. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 89
  8. 2021Explainability-based Backdoor Attacks Against Graph Neural Networkscited by 42
  9. 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
  10. 2020GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networkscited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2023Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Networkcited by 4
  2. 2024DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networkscited by 0
  3. 2024Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learningcited by 0
  4. 2026BadImplant: Injection-based Multi-Targeted Graph Backdoor Attackcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
  2. 2023Unnoticeable Backdoor Attacks on Graph Neural Networks
  3. 2024Effective backdoor attack on graph neural networks in link prediction tasks
  4. 2026Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors
  5. 2025Towards Effective, Stealthy, and Persistent Backdoor Attacks Targeting Graph Foundation Models
  6. 2025Boosting Graph Robustness Against Backdoor Attacks: An Over-Similarity Perspective
  7. 2022Defending Against Backdoor Attack on Graph Nerual Network by Explainability
  8. 2026Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks