mlsanityData to 14 Oct 2026

Privacy and Transparency in Graph Machine Learning: A Unified Perspective

arXiv 2207.10896 ↗First posted 22 Jul 2022cs.LG cs.CRcited by 5

Not on the map. Why?

Abstract (arXiv)

Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of models and their applications in several domains. With its growing applicability to sensitive domains and regulations by governmental agencies for trustworthy AI systems, researchers have started looking into the issues of transparency and privacy of graph learning. However, these topics have been mainly investigated independently. In this position paper, we provide a unified perspective on the interplay of privacy and transparency in GraphML. In particular, we describe the challenges and possible research directions for a formal investigation of privacy-transparency tradeoffs in GraphML.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 37 of its 54 references that are in the map. First posted on arXiv 22 Jul 2022.

Builds on

37 of 54 references.

References · 37

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  4. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  5. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  6. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  7. 2020Learning to Simulate Complex Physics with Graph Networkscited by 880
  8. 2020Parameterized Explainer for Graph Neural Networkcited by 426
  9. 2018Learning to Explain: An Information-Theoretic Perspective on Model Interpretationcited by 248
  10. 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
  11. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
  12. 2016Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layerscited by 199
25 more
  1. 2022Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanismcited by 171
  2. 2020Graph Information Bottleneck for Subgraph Recognitioncited by 119
  3. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  4. 2020Stealing Links from Graph Neural Networkscited by 115
  5. 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
  6. 2021ProtGNN: Towards Self-Explaining Graph Neural Networkscited by 96
  7. 2021Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Datacited by 87
  8. 2020Quantifying Privacy Leakage in Graph Embeddingcited by 82
  9. 2021Membership Inference Attack on Graph Neural Networkscited by 81
  10. 2021Robust Counterfactual Explanations on Graph Neural Networkscited by 72
  11. 2020Locally Private Graph Neural Networkscited by 64
  12. 2021GraphMI: Extracting Private Graph Data from Graph Neural Networkscited by 50
  13. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  14. 2021Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networkscited by 35
  15. 2021Releasing Graph Neural Networks with Differential Privacy Guaranteescited by 32
  16. 2020Adversarial Privacy Preserving Graph Embedding against Inference Attackcited by 21
  17. 2020Information Obfuscation of Graph Neural Networkscited by 19
  18. 2018Node Representation Learning for Directed Graphscited by 16
  19. 2022Private Graph Extraction via Feature Explanationscited by 16
  20. 2021NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Datacited by 13
  21. 2022BAGEL: A Benchmark for Assessing Graph Neural Network Explanationscited by 11
  22. 2019ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictionscited by 10
  23. 2019Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Basescited by 7
  24. 2021Learnt Sparsification for Interpretable Graph Neural Networkscited by 4
  25. 2021MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Predictioncited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 1.

5 papers.

  1. 2022Explaining the Explainers in Graph Neural Networks: a Comparative Studycited by 35
  2. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  3. 2024Model Inversion Attacks: A Survey of Approaches and Countermeasurescited by 11
  4. 2024A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasurescited by 6
  5. 2026Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defensecited by 0