GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models | Paper Citation Record

Observation 859567cc-cc23-4c6e-ac44-ae7ada09e590 · outbound

This paper cites https://https://github.com/tkipf/gcn.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models https://https://github.com/tkipf/gcn

Reference 1

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Observation ec8a9235-3ebc-44ee-9f8d-4735646bc864 · outbound

This paper cites https://github.com/xinle ihe/link stealing attack.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models https://github.com/xinle ihe/link stealing attack

Reference 2

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This paper cites https://github.com/PetarV-/GAT, 2017.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models https://github.com/PetarV-/GAT, 2017

Reference 3

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This paper cites Deep learning with differential privacy.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Deep learning with differential privacy

Reference 4

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This paper cites Structural, Syntactic, and Statistical Pattern Recognition.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Structural, Syntactic, and Statistical Pattern Recognition

Reference 5

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Observation c09b8231-b5d0-4474-93f0-0ee3c158deaf · outbound

This paper cites Extracting training data from large language models.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Extracting training data from large language models

Reference 6

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Observation 7b620eaf-64e4-4cf4-9448-dfe3196fb473 · outbound

This paper cites Exploring connections between active learning and model extraction.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Exploring connections between active learning and model extraction

Reference 7

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Observation b8c62a11-7fdc-404d-9f8e-016d23e9119f · outbound

This paper cites Label-only membership inference attacks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Label-only membership inference attacks

Reference 8

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Observation dbcbb3da-204e-42b8-9451-969f5f054bd7 · outbound

This paper cites Distinguishing enzyme structures from non-enzymes without alignments.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Distinguishing enzyme structures from non-enzymes without alignments

Reference 9

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Observation 0394d47a-309c-4536-87c5-6b44552aa5b0 · outbound

This paper cites Inductive repre- sentation learning on large graphs.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Inductive repre- sentation learning on large graphs

Reference 10

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Observation 0882f329-d60a-4d9d-94b2-1fd348afa44e · outbound

This paper cites Stealing links from graph neural networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Stealing links from graph neural networks

Reference 11

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Observation ac41a6bb-1007-4b66-a2a4-f0575081cf18 · outbound

This paper cites Node-Level Membership Inference Attacks Against Graph Neural Networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Node-Level Membership Inference Attacks Against Graph Neural Networks

Reference 12

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This paper cites Transmia: membership inference attacks using transfer shadow training.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Transmia: membership inference attacks using transfer shadow training

Reference 13

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This paper cites High accuracy and high fidelity extraction of neural networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models High accuracy and high fidelity extraction of neural networks

Reference 14

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This paper cites Memguard: Defending against black-box membership inference attacks via adversarial examples.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Memguard: Defending against black-box membership inference attacks via adversarial examples

Reference 15

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This paper cites Semi-supervised classification with graph convolutional networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Semi-supervised classification with graph convolutional networks

Reference 16

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Observation fbba1405-11b9-48ce-9cf0-2561366e9c34 · outbound

This paper cites Stolen memories: Leveraging model memorization for calibrated {White-Box} membership inference.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Stolen memories: Leveraging model memorization for calibrated {White-Box} membership inference

Reference 17

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Observation 49a3d0fd-2e0e-440d-afbc-50966cddc216 · outbound

This paper cites Membership inference attacks and defenses in classification models.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Membership inference attacks and defenses in classification models

Reference 18

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Observation 8131b299-e17e-4d43-bdc9-a44eb29f4b13 · outbound

This paper cites Membership leakage in label-only exposures.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Membership leakage in label-only exposures

Reference 19

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This paper cites Encodermi: Membership inference against pre-trained encoders in contrastive learning.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Encodermi: Membership inference against pre-trained encoders in contrastive learning

Reference 20

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Observation 8d35d4e7-a166-4c00-b39c-e34cce40191f · outbound

This paper cites Devil in disguise: Breaching graph neural networks privacy through infiltration.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Devil in disguise: Breaching graph neural networks privacy through infiltration

Reference 21

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This paper cites Comprehensive pri- vacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Comprehensive pri- vacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 22

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This paper cites Machine learning with membership privacy using adversarial regularization.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Machine learning with membership privacy using adversarial regularization

Reference 23

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Observation 0c76d516-9628-4637-b207-bd256fd72477 · outbound

This paper cites Membership inference attack on graph neural networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Membership inference attack on graph neural networks

Reference 24

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This paper cites White-box vs black-box: Bayes optimal strategies for membership inference.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models White-box vs black-box: Bayes optimal strategies for membership inference

Reference 25

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This paper cites Gap: Differentially private graph neural net- works with aggregation perturbation.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Gap: Differentially private graph neural net- works with aggregation perturbation

Reference 26

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Observation dc8a0da1-8c50-413a-9129-c908bde357ca · outbound

This paper cites Gap: Differentially private graph neural net- works with aggregation perturbation.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Gap: Differentially private graph neural net- works with aggregation perturbation

Reference 27

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Observation 832e6fa3-b5e1-406e-8c5c-86c1b483c757 · outbound

This paper cites Updates-leak: Data set inference and reconstruction attacks in online learning.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Updates-leak: Data set inference and reconstruction attacks in online learning

Reference 28

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This paper cites Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models

Reference 29

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This paper cites The graph neural network model.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models The graph neural network model

Reference 30

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This paper cites Privacy-preserving deep learning.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Privacy-preserving deep learning

Reference 31

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This paper cites Membership inference attacks against machine learning models.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Membership inference attacks against machine learning models

Reference 32

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GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Information leakage in embedding models

Reference 33

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This paper cites Systematic evaluation of privacy risks of machine learning models.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Systematic evaluation of privacy risks of machine learning models

Reference 34

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Observation 35c97d71-cbb6-4115-94f8-6156f2a458d4 · outbound

This paper cites Privacy risks of securing machine learning models against adversarial examples.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Privacy risks of securing machine learning models against adversarial examples

Reference 35

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This paper cites Dropout: a simple way to prevent neural networks from overfitting.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Dropout: a simple way to prevent neural networks from overfitting

Reference 36

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GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Stealing machine learning models via prediction {APIs}

Reference 37

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This paper cites Graph attention networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Graph attention networks

Reference 38

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This paper cites Stealing hyperparameters in machine learning.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Stealing hyperparameters in machine learning

Reference 39

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This paper cites Differentially private empirical risk minimization revisited: Faster and more general.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Differentially private empirical risk minimization revisited: Faster and more general

Reference 40

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This paper cites Link membership inference at- tacks against unsupervised graph representation learning.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Link membership inference at- tacks against unsupervised graph representation learning

Reference 41

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This paper cites Semi-supervised classification with graph convolutional networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Semi-supervised classification with graph convolutional networks

Reference 42

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This paper cites Vertex cover.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Vertex cover

Reference 43

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Observation c091bcee-2c9d-4510-949d-b9a16171c08b · outbound

This paper cites Adapting membership inference attacks to gnn for graph classification: Ap- proaches and implications.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Adapting membership inference attacks to gnn for graph classification: Ap- proaches and implications

Reference 44

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Observation 8a3a91d1-aaeb-44c6-8c31-9376b755ad20 · outbound

This paper cites Linkteller: Recovering private edges from graph neural networks via influence analysis.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Linkteller: Recovering private edges from graph neural networks via influence analysis

Reference 45

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Observation 3e6d8192-7608-46df-a698-24196fde3cfc · outbound

This paper cites Link Stealing Attacks Against Inductive Graph Neural Networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Link Stealing Attacks Against Inductive Graph Neural Networks

Reference 46

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This paper cites How powerful are graph neural networks? In International Conference on Learning Representations (ICLR) , 2019.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models How powerful are graph neural networks? In International Conference on Learning Representations (ICLR) , 2019

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:53:16.015151Z

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This paper cites Differentially private model publishing for deep learning.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Differentially private model publishing for deep learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:53:15.999261Z

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Observation d63a667c-6c81-4a76-8c0c-7425d25be10f · outbound

This paper cites Graph transformer networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Graph transformer networks

Reference 49

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This paper cites Demystifying uneven vulnerability of link stealing attacks against graph neural networks.

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Demystifying uneven vulnerability of link stealing attacks against graph neural networks

Reference 50

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