mlsanityData to 7 Oct 2026

Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem

arXiv 2106.10785 ↗First posted 21 Jun 2021cs.LG cs.AIcited by 20

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Abstract (arXiv)

Graph neural networks (GNNs) have attracted increasing interests. With broad deployments of GNNs in real-world applications, there is an urgent need for understanding the robustness of GNNs under adversarial attacks, especially in realistic setups. In this work, we study the problem of attacking GNNs in a restricted and realistic setup, by perturbing the features of a small set of nodes, with no access to model parameters and model predictions. Our formal analysis draws a connection between this type of attacks and an influence maximization problem on the graph. This connection not only enhances our understanding on the problem of adversarial attack on GNNs, but also allows us to propose a group of effective and practical attack strategies. Our experiments verify that the proposed attack strategies significantly degrade the performance of three popular GNN models and outperform baseline adversarial attack strategies.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 25 of its 38 references that are in the map. First posted on arXiv 21 Jun 2021.

Builds on

25 of 38 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  7. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  8. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  9. 2019Graph Neural Networks for Social Recommendationcited by 711
  10. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  11. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  12. 2018Adversarial Attack on Graph Structured Datacited by 300
13 more
  1. 2014The Loss Surfaces of Multilayer Networkscited by 278
  2. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  3. 2016Deep Learning without Poor Local Minimacited by 233
  4. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  5. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 166
  6. 2018Adversarial Attacks on Node Embeddings via Graph Poisoningcited by 109
  7. 2020Towards More Practical Adversarial Attacks on Graph Neural Networkscited by 76
  8. 2019A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Modelscited by 68
  9. 2016DeepCas: an End-to-end Predictor of Information Cascadescited by 48
  10. 2020Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studiescited by 42
  11. 2020Practical No-box Adversarial Attacks against DNNscited by 32
  12. 2019Node Injection Attacks on Graphs via Reinforcement Learningcited by 15
  13. 2020Adversarial Attack on Hierarchical Graph Pooling Neural Networkscited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 5; 2024: 8; 2025: 1; 2026: 2.

20 papers.

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  1. 2022Adversarial Graph Contrastive Learning with Information Regularizationcited by 38
  2. 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approachcited by 24
  3. 2022A Survey on Influence Maximization: From an ML-Based Combinatorial Optimizationcited by 15
  4. 2022MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distributioncited by 11
  5. 2024Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Studycited by 9
  6. 2022ARIEL: Adversarial Graph Contrastive Learningcited by 5
  7. 2023Adversarial Attacks on Fairness of Graph Neural Networkscited by 4
  8. 2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Datacited by 4
  9. 2023Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learningcited by 3
  10. 2023Everything Perturbed All at Once: Enabling Differentiable Graph Attackscited by 2
  11. 2024Fast Inference of Removal-Based Node Influencecited by 2
  12. 2024RIDA: A Robust Attack Framework on Incomplete Graphscited by 1
8 more
  1. 2024GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximizationcited by 1
  2. 2023Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detectioncited by 0
  3. 2024Inferring Properties of Graph Neural Networkscited by 0
  4. 2024On the Adversarial Robustness of Graph Neural Networks with Graph Reductioncited by 0
  5. 2024Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectorscited by 0
  6. 2025Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentationscited by 0
  7. 2026Adversarial Attacks on Locally Private Graph Neural Networkscited by 0
  8. 2026Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraintscited by 0