GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning | Paper Citation Record

Observation 9821ddf3-bb31-45ba-a5b0-76c1bf4e325e · outbound

This paper cites Graph Neural Networks: Methods, Applications, and Opportunities.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Graph Neural Networks: Methods, Applications, and Opportunities

Reference 1

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Observation c04cd7bb-9655-4fe9-a2cd-bfb1f0d7d737 · outbound

This paper cites Local differential private spatio- temporal dynamic graph learning for wireless social networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Local differential private spatio- temporal dynamic graph learning for wireless social networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.655301Z

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Observation b9936d15-2af0-42c9-ab1d-be5f44af4e9f · outbound

This paper cites Relevance-aware anomalous users detection in social network via graph neural network,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Relevance-aware anomalous users detection in social network via graph neural network,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.631309Z

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Observation 2ba4bfe5-01fe-4a4e-b3a7-058e5f3d8d8f · outbound

This paper cites LR-GNN: a graph neural network based on link representation for predicting molecular associations,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning LR-GNN: a graph neural network based on link representation for predicting molecular associations,

Reference 4

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Observation fbc56bf3-27ca-4f9a-8d0c-bfa7f365cc89 · outbound

This paper cites Pre-training graph neural networks for link prediction in biomedical networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Pre-training graph neural networks for link prediction in biomedical networks,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.160582Z

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Observation 21ab3643-cfa9-40ad-b927-2c8c0cb0ace2 · outbound

This paper cites Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation,

Reference 6

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Observation e360f4ef-a5c2-4063-b048-c0bbd7b09aba · outbound

This paper cites Dskreg: Differentiable sampling on knowledge graph for recommendation with relational gnn,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Dskreg: Differentiable sampling on knowledge graph for recommendation with relational gnn,

Reference 7

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Observation da934daa-e9e1-4d3a-bbf5-269a0612c2fa · outbound

This paper cites Prioritizing network communities,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Prioritizing network communities,

Reference 8

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Observation 62bac7e7-7419-4d50-8bf2-9c2dc1cca207 · outbound

This paper cites Demystifying multitask deep neural networks for quantitative structure–activity relationships,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Demystifying multitask deep neural networks for quantitative structure–activity relationships,

Reference 9

Resolution: verified exact; doi, observed 2026-08-12T15:02:32.574871Z

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Observation 310fb90b-b517-46ec-b81a-2eb9f1beed01 · outbound

This paper cites Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.190370Z

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Observation 3f3e2ae7-3649-4aaa-8454-3165a2570106 · outbound

This paper cites All in one: Multi-task prompting for graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning All in one: Multi-task prompting for graph neural networks,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.195847Z

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Observation b0d61029-626b-4183-a744-8140f9e89360 · outbound

This paper cites Gppt: Graph pre-training and prompt tuning to generalize graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Gppt: Graph pre-training and prompt tuning to generalize graph neural networks,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.200682Z

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Observation e47e95a6-f25c-4d15-a413-5a1d2ad69694 · outbound

This paper cites Graphprompt: Unifying pre-training and downstream tasks for graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Graphprompt: Unifying pre-training and downstream tasks for graph neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.598416Z

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Observation 1edfc88d-8a62-42ab-8ee7-dd44a28ed34f · outbound

This paper cites Universal prompt tuning for graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Universal prompt tuning for graph neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.575327Z

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Observation fcb199b3-45aa-4ed2-892f-b08268c2d9f3 · outbound

This paper cites Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learn- ing,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learn- ing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.557770Z

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Observation cff27c8a-aaf7-4322-91d3-02f64bcee8d1 · outbound

This paper cites Multigprompt for multi-task pre-training and prompting on graphs,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Multigprompt for multi-task pre-training and prompting on graphs,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.523249Z

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Observation d51d5bf3-521f-4639-bec5-d9930d0682bf · outbound

This paper cites Prompt Engineering a Prompt Engineer.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Prompt Engineering a Prompt Engineer

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.226115Z

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Observation bf380c03-3014-4f5c-9c0d-ea0644a89f86 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.233674Z

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Observation 366eff6b-ecd9-4e22-b33e-37ab01b0ab5a · outbound

This paper cites Locally private graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Locally private graph neural networks,

Reference 19

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Observation e3f4ec47-e79d-4d08-be25-0bc2b72a611d · outbound

This paper cites Gap: differentially private graph neural networks with aggregation perturbation,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Gap: differentially private graph neural networks with aggregation perturbation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.506498Z

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Observation a9d73d22-c07a-4c50-ac0e-36652388077b · outbound

This paper cites Differentially private decoupled graph convolutions for multigranular topology protection,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Differentially private decoupled graph convolutions for multigranular topology protection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.484792Z

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Observation 5f30599f-bdd1-49ad-9bb5-e79289874b3d · outbound

This paper cites Linkguard: Link locally privacy-preserving graph neural networks with integrated denoising and private learning,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Linkguard: Link locally privacy-preserving graph neural networks with integrated denoising and private learning,

Reference 22

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Observation 44050ac6-193d-4503-a603-63424cff5c67 · outbound

This paper cites Lingcn: structural lin- earized graph convolutional network for homomorphically encrypted inference,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Lingcn: structural lin- earized graph convolutional network for homomorphically encrypted inference,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.462734Z

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Observation 9a792ed2-535f-4934-abcb-d90a6087b3e4 · outbound

This paper cites Pre-trained Models for Natural Language Processing: A Survey.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Pre-trained Models for Natural Language Processing: A Survey

Reference 25

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Observation 333baff3-ee65-45c2-ae15-3d9b028f5e09 · outbound

This paper cites SciBERT: A pretrained language model for scientific text,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning SciBERT: A pretrained language model for scientific text,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.438154Z

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Observation 3d5bbb44-52cd-496e-b4a5-1d1d19be0ba7 · outbound

This paper cites Vision-and-Language Pretrained Models: A Survey.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Vision-and-Language Pretrained Models: A Survey

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.285130Z

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Observation 1b769cb5-7e55-4e72-9b84-1eb670edcc87 · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning BEiT: BERT Pre-Training of Image Transformers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.289558Z

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Observation fe82c18e-eb9a-4235-bcfd-1ca60e19a403 · outbound

This paper cites A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.293362Z

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Observation a0613577-2e76-4be6-8444-9b249c065876 · outbound

This paper cites Strategies for pre-training graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Strategies for pre-training graph neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.418964Z

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Observation b852dbcb-897d-4ab0-92fb-da5b28407b7a · outbound

This paper cites Deep Graph Infomax.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Deep Graph Infomax

Reference 31

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Observation 2f23aa93-258e-4cff-a940-acd5973b74d0 · outbound

This paper cites Variational Graph Auto-Encoders.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Variational Graph Auto-Encoders

Reference 32

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Observation 4be44cbe-af97-44a9-b9aa-62368fc271dd · outbound

This paper cites Graphmae: Self-supervised masked graph autoencoders,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Graphmae: Self-supervised masked graph autoencoders,

Reference 33

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Observation ef8fa5d4-372d-4b07-9d06-7a8f6502ae0e · outbound

This paper cites Graph contrastive learning with augmentations,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Graph contrastive learning with augmentations,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.395953Z

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Observation 5a687bf1-e5b8-4b01-806b-f533917dcbe4 · outbound

This paper cites Simgrace: A simple framework for graph contrastive learning without data augmentation,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Simgrace: A simple framework for graph contrastive learning without data augmentation,

Reference 35

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Observation 5e31e98a-2f39-4ea4-9800-0f67db7cf1bc · outbound

This paper cites Prog: A graph prompt learning benchmark,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Prog: A graph prompt learning benchmark,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.377083Z

Source-reported events for the cited work

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Observation 06530568-7d36-4f57-a54d-ec26629f323f · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.336727Z

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Observation 3db8ccc3-8c53-4404-a4ce-82cca2f43c5b · outbound

This paper cites Membership inference attacks against machine learning models,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Membership inference attacks against machine learning models,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.328028Z

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Observation d5232e98-282c-4699-9938-aa8d6bedbcd9 · outbound

This paper cites Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.307235Z

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Observation eb3ac8cf-f634-4df4-8d63-482a77378463 · outbound

This paper cites Random forests,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Random forests,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.274090Z

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Observation 22b2e3c5-9651-4778-8535-5288bb61c293 · outbound

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

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Node-Level Membership Inference Attacks Against Graph Neural Networks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.367591Z

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Observation 3e6c0bd4-b303-4402-a63e-ead9fada1ef1 · outbound

This paper cites Deep Graph Infomax,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Deep Graph Infomax,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.233865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bced7818-a747-4601-a040-40aa7ecdaad8 · outbound

This paper cites Stealing Links from Graph Neural Networks.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Stealing Links from Graph Neural Networks

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:02:33.159053Z

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Observation d570ffa9-8a21-4b86-8bc4-7be574c3df41 · outbound

This paper cites Data fine-tuning,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Data fine-tuning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.220054Z

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Observation 3e3af275-13d1-451b-9cb3-51230962e1f9 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Universal Language Model Fine-tuning for Text Classification

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.386289Z

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Observation 04d7a552-8212-4e79-850c-579dd0e75b62 · outbound

This paper cites SpotTune: Transfer Learning through Adaptive Fine-tuning.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning SpotTune: Transfer Learning through Adaptive Fine-tuning

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:02:33.107035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 32d475c1-772f-44fc-86fb-646735dd1da6 · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.395523Z

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Observation 1327ebb9-c7a3-4664-b520-627f83ce239f · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.400200Z

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Observation 3aef8a5a-e3ec-46d0-9550-16c6da3fd219 · outbound

This paper cites Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.405286Z

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Observation 984c5862-a7fe-48fc-8962-af041bad7a2c · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.410155Z

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Observation 73123378-8686-48e2-a650-4791a5f534db · outbound

This paper cites Visual Prompting via Image Inpainting.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Visual Prompting via Image Inpainting

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.415020Z

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Observation 2c6541eb-2209-417b-a315-a805464e27bf · outbound

This paper cites Diversity-Aware Meta Visual Prompting.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Diversity-Aware Meta Visual Prompting

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:02:32.970671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 87e39325-a6c6-4bc5-9692-84847305fa11 · outbound

This paper cites SGL-PT: A Strong Graph Learner with Graph Prompt Tuning.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning SGL-PT: A Strong Graph Learner with Graph Prompt Tuning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.424930Z

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Observation 49cd226c-1032-455d-98e0-d568cb20074f · outbound

This paper cites Hetgpt: Harnessing the power of prompt tuning in pre-trained heterogeneous graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Hetgpt: Harnessing the power of prompt tuning in pre-trained heterogeneous graph neural networks,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.429731Z

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Observation 48e0946c-de39-4a7a-8b37-de487f9bd636 · outbound

This paper cites Ultra-dp: Unifying graph pre-training with multi-task graph dual prompt,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Ultra-dp: Unifying graph pre-training with multi-task graph dual prompt,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.203823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df7f8718-3fa7-47ad-be34-7b176c246a96 · outbound

This paper cites Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.447972Z

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Observation 190748b9-0e80-454b-9a11-4fa9857f9751 · outbound

This paper cites PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:02:32.814383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 123b1db7-02fb-42c3-b62f-01eb79d3bc90 · outbound

This paper cites Inference attacks against graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Inference attacks against graph neural networks,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.189417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 66ce1809-f1ce-4d0d-bf1f-22b8232aa3db · outbound

This paper cites Group property inference attacks against graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Group property inference attacks against graph neural networks,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.170794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 43fcca73-a83c-4ea1-a001-3843cb2e2331 · outbound

This paper cites node2vec: Scalable feature learning for networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning node2vec: Scalable feature learning for networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.153213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation da7ea430-42cf-4799-a342-5a0635b4c53f · outbound

This paper cites Link prediction based on graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Link prediction based on graph neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.136175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d9a04a3c-13c5-448c-886f-5e49adeb5e31 · outbound

This paper cites walk2friends: Inferring social links from mobility profiles,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning walk2friends: Inferring social links from mobility profiles,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.120777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 35ee879d-e87d-4e6e-8df8-edbc7c9a265d · outbound

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

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Linkteller: Recovering private edges from graph neural networks via influence analysis,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.103878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4097159f-1895-49d7-8420-6a157e024fff · outbound

This paper cites Inference attacks against graph neural networks,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Inference attacks against graph neural networks,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.080601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c1a4f4bb-5b81-45b5-8b1d-8c05b32ab7b6 · outbound

This paper cites Quantifying privacy leakage in graph embedding,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Quantifying privacy leakage in graph embedding,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.495398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c45d0a45-5e98-4bbc-b96f-24c4e7284905 · outbound

This paper cites Model extraction attacks on graph neural networks: Taxonomy and realisation,.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Model extraction attacks on graph neural networks: Taxonomy and realisation,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.499520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 275c42b7-3707-498b-8744-cd19dd410882 · outbound

This paper cites Privacy-Preserving Machine Learning: Methods, Challenges and Directions.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.508565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37724444-7f36-4202-97bf-6c225433ec56 · outbound

This paper cites Available: https://www.sciencedirect.com/science/ article/pii/S1352231097004470.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Available: https://www.sciencedirect.com/science/ article/pii/S1352231097004470

Reference 1998

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.290588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c162fe32-cadd-4f56-9f50-bfdeca7f68b3 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 89141.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Available: https://api.semanticscholar.org/CorpusID: 89141

Reference 2001

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.254976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e204a143-256a-4647-9219-5e5296f10956 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 46933970.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Available: https://api.semanticscholar.org/CorpusID: 46933970

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.351278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bff32879-0fe1-4cba-ae18-ba41c0b27ff2 · outbound

This paper cites Strategies for Pre-training Graph Neural Networks.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Strategies for Pre-training Graph Neural Networks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.301729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0782baa3-75b4-42f6-a881-2b50ca48a2ad · outbound

This paper cites Pre-Trained Models: Past, Present and Future.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Pre-Trained Models: Past, Present and Future

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.268449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55604f77-6bae-4c56-9a2e-2bf25d8edf34 · outbound

This paper cites ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.442131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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