mlsanityData to 7 Oct 2026

Reinforcement Learning For Data Poisoning on Graph Neural Networks

arXiv 2102.06800 ↗First posted 12 Feb 2021cs.LG cs.AI cs.CRcited by 3

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Abstract (arXiv)

Adversarial Machine Learning has emerged as a substantial subfield of Computer Science due to a lack of robustness in the models we train along with crowdsourcing practices that enable attackers to tamper with data. In the last two years, interest has surged in adversarial attacks on graphs yet the Graph Classification setting remains nearly untouched. Since a Graph Classification dataset consists of discrete graphs with class labels, related work has forgone direct gradient optimization in favor of an indirect Reinforcement Learning approach. We will study the novel problem of Data Poisoning (training time) attack on Neural Networks for Graph Classification using Reinforcement Learning Agents.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

16 of 30 references.

References · 16

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  5. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  6. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  7. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  8. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 459
  9. 2018Adversarial Attack on Graph Structured Datacited by 300
  10. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 234
  11. 2017Tactics of Adversarial Attack on Deep Reinforcement Learning Agentscited by 163
  12. 2020Backdoor Attacks to Graph Neural Networkscited by 126
4 more
  1. 2016Crafting Adversarial Input Sequences for Recurrent Neural Networkscited by 102
  2. 2020Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studiescited by 42
  3. 2019Attacking Graph Convolutional Networks via Rewiringcited by 32
  4. 2018Spectral Multigraph Networks for Discovering and Fusing Relationships in Moleculescited by 9

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2.

3 papers.

  1. 2022Reinforcement learning on graphs: A surveycited by 18
  2. 2024Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Frameworkcited by 0
  3. 2024Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learningcited by 0