mlsanityData to 7 Oct 2026

VertexSerum: Poisoning Graph Neural Networks for Link Inference

arXiv 2308.01469 ↗First posted 2 Aug 2023cs.LG cs.AI cs.CRcited by 5

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Abstract (arXiv)

Graph neural networks (GNNs) have brought superb performance to various applications utilizing graph structural data, such as social analysis and fraud detection. The graph links, e.g., social relationships and transaction history, are sensitive and valuable information, which raises privacy concerns when using GNNs. To exploit these vulnerabilities, we propose VertexSerum, a novel graph poisoning attack that increases the effectiveness of graph link stealing by amplifying the link connectivity leakage. To infer node adjacency more accurately, we propose an attention mechanism that can be embedded into the link detection network. Our experiments demonstrate that VertexSerum significantly outperforms the SOTA link inference attack, improving the AUC scores by an average of 9.8% across four real-world datasets and three different GNN structures. Furthermore, our experiments reveal the effectiveness of VertexSerum in both black-box and online learning settings, further validating its applicability in real-world scenarios.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 27 of its 37 references that are in the map. First posted on arXiv 2 Aug 2023.

Builds on

27 of 37 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  7. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  8. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
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  11. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
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15 more
  1. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  2. 2019Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networkscited by 241
  3. 2016GRAM: Graph-based Attention Model for Healthcare Representation Learningcited by 152
  4. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  5. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  6. 2020Stealing Links from Graph Neural Networkscited by 114
  7. 2022Truth Serum: Poisoning Machine Learning Models to Reveal Their Secretscited by 94
  8. 2021Membership Inference Attack on Graph Neural Networkscited by 81
  9. 2021LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysiscited by 65
  10. 2022Understanding and Improving Graph Injection Attack by Promoting Unnoticeabilitycited by 62
  11. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  12. 2021GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Networkcited by 37
  13. 2022Amplifying Membership Exposure via Data Poisoningcited by 29
  14. 2021Learning Hierarchical Graph Neural Networks for Image Clusteringcited by 18
  15. 2020Graph Neural Network Encoding for Community Detection in Attribute Networkscited by 17

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Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2025Practical Bayes-Optimal Membership Inference Attackscited by 5
  2. 2024Link Stealing Attacks Against Inductive Graph Neural Networkscited by 2
  3. 2024Inferring Properties of Graph Neural Networkscited by 0
  4. 2025Graph in the Vault: Protecting Edge GNN Inference with Trusted Execution Environmentcited by 0
  5. 2026SoK: Colluding Adversaries in Machine Learning Pipelinescited by 0