mlsanityData to 14 Oct 2026

Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Act

arXiv 2410.22120 ↗First posted 29 Oct 2024cs.LG cs.AI cs.CYcited by 0

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Abstract (arXiv)

The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive guidelines for the development and oversight of Artificial Intelligence (AI) and Machine Learning (ML) systems, with significant implications for Graph Neural Networks (GNNs). This paper addresses the unique challenges posed by the AI Act for GNNs, which operate on complex graph-structured data. The legislation's requirements for data management, data governance, robustness, human oversight, and privacy necessitate tailored strategies for GNNs. Our study explores the impact of these requirements on GNN training and proposes methods to ensure compliance. We provide an in-depth analysis of bias, robustness, explainability, and privacy in the context of GNNs, highlighting the need for fair sampling strategies and effective interpretability techniques. Our contributions fill the research gap by offering specific guidance for GNNs under the new legislative framework and identifying open questions and future research directions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 54 references that are in the map. First posted on arXiv 29 Oct 2024.

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22 of 54 references.

References · 22

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  12. 2021Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methodscited by 36
10 more
  1. 2021Releasing Graph Neural Networks with Differential Privacy Guaranteescited by 32
  2. 2024On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectivescited by 28
  3. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  4. 2022Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNscited by 19
  5. 2022On Structural Explanation of Bias in Graph Neural Networkscited by 15
  6. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 14
  7. 2024Federated Learning Priorities Under the European Union Artificial Intelligence Actcited by 13
  8. 2022SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Servicecited by 10
  9. 2024Graph Neural Networks in EEG-based Emotion Recognition: A Surveycited by 6
  10. 2023Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classificationcited by 1

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