Paper Citation Record · LEDGER

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models

As of 12 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2606.29748.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.29748 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T06:46:58.801354Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact5
verified fuzzy0
unresolved38
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9d3d4494-8adc-43be-b882-8b270e4fccfc · outbound

This paper cites Toward better drug discovery with knowledge graph,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Toward better drug discovery with knowledge graph,

Reference 1

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Observation 88e2b7fa-d92f-4748-97be-13eee7085fd6 · outbound

This paper cites Utilizing graph machine learning within drug discovery and development,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Utilizing graph machine learning within drug discovery and development,

Reference 2

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Observation 531c580d-67f0-460e-bf12-536a75bf550a · outbound

This paper cites Graph pattern matching revised for social network analysis,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Graph pattern matching revised for social network analysis,

Reference 3

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Observation 104b83f9-acb4-4f64-8794-e1ad93332332 · outbound

This paper cites A machine learning approach for predicting hidden links in supply chain with graph neural networks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models A machine learning approach for predicting hidden links in supply chain with graph neural networks,

Reference 4

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Observation efb6d220-c55e-419e-9e66-11df3e5781e3 · outbound

This paper cites Machine learning methods in finance: Recent applications and prospects,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Machine learning methods in finance: Recent applications and prospects,

Reference 5

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Observation d4caf6ff-cf3a-4188-8141-131d852baa77 · outbound

This paper cites Preserving data privacy in machine learning systems,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Preserving data privacy in machine learning systems,

Reference 6

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Observation bf315643-643f-4797-a1dd-91caa18c12f8 · outbound

This paper cites Understanding stability of choices: Toward robust choice-based authentication in cybersecurity,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Understanding stability of choices: Toward robust choice-based authentication in cybersecurity,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

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Observation dc0a0804-f9b1-4f86-9ce5-160df8ecd089 · outbound

This paper cites An overview on the application of graph neural networks in wireless networks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models An overview on the application of graph neural networks in wireless networks,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

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Observation 67553303-ef01-43b0-85bd-d6612e681dff · outbound

This paper cites Adoption of machine learning in pharmacometrics: an overview of recent implementations and their considerations,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Adoption of machine learning in pharmacometrics: an overview of recent implementations and their considerations,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

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Observation db5fdbe4-1b8e-4544-9f65-f1c81f6cd64d · outbound

This paper cites Opinion leaders for information diffusion using graph neural network in online social networks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Opinion leaders for information diffusion using graph neural network in online social networks,

Reference 10

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Observation a92b3f82-f58c-402b-b69c-ab12e0ad1fc6 · outbound

This paper cites GraphMI: Extracting Private Graph Data from Graph Neural Networks.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models GraphMI: Extracting Private Graph Data from Graph Neural Networks

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:54:20.823719Z

Source-reported events for the cited work

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Observation 7da02b43-3f0d-4ec9-a7a2-cbdf10826549 · outbound

This paper cites Model Inversion Attacks: A Survey of Approaches and Countermeasures.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Model Inversion Attacks: A Survey of Approaches and Countermeasures

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:54:20.830507Z

Source-reported events for the cited work

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Observation e88cf0b3-2f28-484c-b485-7180581f0a7b · outbound

This paper cites Adversarial attacks on graph neural networks: Perturbations and their patterns,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Adversarial attacks on graph neural networks: Perturbations and their patterns,

Reference 13

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Observation 3c5776ba-6942-40a7-85ff-c2ccf6b31c22 · outbound

This paper cites Ad- versarial attack on graph structured data,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Ad- versarial attack on graph structured data,

Reference 14

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Source-reported events for the cited work

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Observation e3ad7b9a-00df-47fb-aeed-3d6c7f58ea14 · outbound

This paper cites Privacy in pharmacogenetics: An{End-to-End}case study of person- alized warfarin dosing,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Privacy in pharmacogenetics: An{End-to-End}case study of person- alized warfarin dosing,

Reference 15

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Observation a3ca0261-fec8-4b48-8a25-5d449e82fb2e · outbound

This paper cites Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes,

Reference 16

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Source-reported events for the cited work

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Observation 264bfe0c-3166-43a7-9887-3cc91fa8e287 · outbound

This paper cites SoK: model inversion attack landscape: Taxonomy, challenges, and future roadmap,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models SoK: model inversion attack landscape: Taxonomy, challenges, and future roadmap,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

Source-reported events for the cited work

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Observation ea67928d-823e-4701-8d35-1e1f867a5755 · outbound

This paper cites The secret revealer: Generative model-inversion attacks against deep neural net- works,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models The secret revealer: Generative model-inversion attacks against deep neural net- works,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

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Observation b5999a46-66f1-4ab5-b4ec-7323af5b71cf · outbound

This paper cites Improved techniques for model inversion attacks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Improved techniques for model inversion attacks,

Reference 19

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Source-reported events for the cited work

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Observation fa377b15-9a00-41cb-b6ff-9a5c8673dbe6 · outbound

This paper cites Re-thinking model inversion attacks against deep neural net- works,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Re-thinking model inversion attacks against deep neural net- works,

Reference 20

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Source-reported events for the cited work

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Observation f9f4a532-8751-4ef5-8401-824d8e4ca130 · outbound

This paper cites A new federated learning framework against gradient inversion attacks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models A new federated learning framework against gradient inversion attacks,

Reference 21

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Source-reported events for the cited work

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Observation 0655e045-48e1-49d8-a30e-f1852a7e7e41 · outbound

This paper cites Model inversion attacks against graph neural networks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Model inversion attacks against graph neural networks,

Reference 22

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Observation 89057f1c-174c-4a1d-ae57-922695cc99c5 · outbound

This paper cites Model inversion attacks against collaborative inference,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Model inversion attacks against collaborative inference,

Reference 23

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Observation 27313868-f154-4f73-8c5c-43eb27ec394e · outbound

This paper cites Inference attacks against graph neural networks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Inference attacks against graph neural networks,

Reference 24

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Observation e9fbfeba-12c4-45ef-a1ab-7acd86b5e7db · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Model inversion attacks that exploit confidence information and basic countermeasures,

Reference 25

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Observation 11ddb2a5-85ad-4e29-a66a-ca3ec85039a5 · outbound

This paper cites A methodology for formalizing model-inversion attacks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models A methodology for formalizing model-inversion attacks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

Source-reported events for the cited work

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Observation 557d7553-db41-49f5-b9af-2f683387a84a · outbound

This paper cites Model Inversion Attacks Through Target-Specific Conditional Diffusion Models.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Model Inversion Attacks Through Target-Specific Conditional Diffusion Models

Reference 27

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Source-reported events for the cited work

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Observation 9965388f-85c6-4712-985a-a29904502dc7 · outbound

This paper cites Variational model inversion attacks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Variational model inversion attacks,

Reference 28

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Source-reported events for the cited work

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Observation cb9503b4-56e8-44bd-82a6-cb0ae67c7338 · outbound

This paper cites Quantifying Privacy Leakage in Graph Embedding.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Quantifying Privacy Leakage in Graph Embedding

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:54:20.822453Z

Source-reported events for the cited work

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Observation 5c148fc1-2bfa-4669-bd22-95f06d47347b · outbound

This paper cites Privacy risks of llm-empowered recommender systems: An inversion attack perspective,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Privacy risks of llm-empowered recommender systems: An inversion attack perspective,

Reference 30

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Source-reported events for the cited work

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Observation 4c00c2ef-cebb-4aa1-954a-b19e9e838fa2 · outbound

This paper cites Prompt inversion attack against collaborative inference of large language mod- els,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Prompt inversion attack against collaborative inference of large language mod- els,

Reference 31

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Observation ae8ec475-95d0-4237-ad68-659ab6e21836 · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Model inversion attacks that exploit confidence information and basic countermeasures,

Reference 32

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Observation 3387e70c-3a20-410d-a89a-c7bfce613f14 · outbound

This paper cites Adversarial neural network inversion via auxiliary knowledge alignment,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Adversarial neural network inversion via auxiliary knowledge alignment,

Reference 33

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Observation 1d1baab7-ccb5-472c-a8e2-eaf67f5f912d · outbound

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Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Mirror: Model inversion for deep learningnetwork with high fidelity,

Reference 34

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Observation c2baf0ac-b2c9-456b-9c1d-48fce980c24c · outbound

This paper cites Defending the graph reconstruction attacks for simplicial neural networks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Defending the graph reconstruction attacks for simplicial neural networks,

Reference 35

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This paper cites Generative adversarial networks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Generative adversarial networks,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

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Observation 24198daf-7e19-436e-a7e0-0bf186e7a708 · outbound

This paper cites TUDataset: A collection of benchmark datasets for learning with graphs.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models TUDataset: A collection of benchmark datasets for learning with graphs

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-06-30T06:54:20.833284Z

Source-reported events for the cited work

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Observation fd2bd431-1fdc-492d-ae54-fc69bc5b6298 · outbound

This paper cites On the relation between graph distance and euclidean distance in random geometric graphs,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models On the relation between graph distance and euclidean distance in random geometric graphs,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

Source-reported events for the cited work

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Observation e9252b31-f62a-477d-aead-279e2612c342 · outbound

This paper cites A continuous structural intervention distance to compare causal graphs,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models A continuous structural intervention distance to compare causal graphs,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

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Observation 6d631e99-56da-4944-997d-06d035045e77 · outbound

This paper cites A generalized weisfeiler-lehman graph kernel,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models A generalized weisfeiler-lehman graph kernel,

Reference 40

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This paper cites Predicting a user’s de- mographic identity from leaked samples of health-tracking wearables and understanding associated risks,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Predicting a user’s de- mographic identity from leaked samples of health-tracking wearables and understanding associated risks,

Reference 41

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Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Network intrusion detection,

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Observation 75b1b14a-25ee-412d-b6b0-e2c0a115568b · outbound

This paper cites Deriving college students’ phone call patterns to improve student life,.

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models Deriving college students’ phone call patterns to improve student life,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-30T06:46:58.801354Z

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Pith citing papers

No inbound Pith citation observations are available.