Paper Citation Record · LEDGER

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks

As of 22 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.20074.

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

pith.paper-citation-record.v1
2505.20074 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:05:02.879506Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

33 of 33 outbound references displayed

verified exact2
verified fuzzy22
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a4a102f-ea4c-4e7f-938a-eebf7b9692e4 · outbound

This paper cites Invariant Risk Minimization.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Invariant Risk Minimization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:58.603852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97b756b0-6851-4ec7-b95d-8a90264b37d4 · outbound

This paper cites Out-of-distribution generalization via risk extrapolation (rex).

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Out-of-distribution generalization via risk extrapolation (rex)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:07.583929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53360b24-acb2-4db9-98a8-76faaae2f22c · outbound

This paper cites Learning invariant graph representations for out-of-distribution generalization.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Learning invariant graph representations for out-of-distribution generalization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:07.358902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 278e2587-af00-4c87-ab6e-58f19bd1e90c · outbound

This paper cites Towards Out-Of-Distribution Generalization: A Survey.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Towards Out-Of-Distribution Generalization: A Survey

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:00.285627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80bfed05-2fa4-4087-b550-da05d121a441 · outbound

This paper cites Flood: A flex- ible invariant learning framework for out-of-distribution generalization on graphs.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Flood: A flex- ible invariant learning framework for out-of-distribution generalization on graphs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:06.968429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 45e7543e-eb9a-4469-8538-f7efa1bbf45b · outbound

This paper cites Membership inference attack on graph neural networks.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Membership inference attack on graph neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:06.726003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 472f8a3f-e094-4c61-93eb-8a73f06e4b53 · outbound

This paper cites Do imagenet classi- fiers generalize to imagenet? In International conference on machine learning, pages 5389–5400.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Do imagenet classi- fiers generalize to imagenet? In International conference on machine learning, pages 5389–5400

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:06.521411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation db0daab7-5d30-4d4b-a625-1a97248189d2 · outbound

This paper cites Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:00.625722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89e4c090-4884-4fc8-aa6b-264d1468e84f · outbound

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

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:00.773246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdc61016-bbb3-4ca5-ad9a-e6461f251fd2 · outbound

This paper cites Collective classification in network data.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Collective classification in network data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:06.332443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 802ed4a7-917a-44fb-a09e-7435e70d9085 · outbound

This paper cites Membership infer- ence attacks against machine learning models.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Membership infer- ence attacks against machine learning models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:05.917309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7a5c7c90-c268-4fa9-b48f-f78cd81bd119 · outbound

This paper cites Auditing data provenance in text-generation models.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Auditing data provenance in text-generation models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:05.708763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3a048b4d-203e-4f4f-b807-be67478e1703 · outbound

This paper cites Social structure of facebook networks.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Social structure of facebook networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:05.476940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b30d761b-b8a1-4b66-b6e0-85cd9ecf78ad · outbound

This paper cites Principles of risk mini- mization for learning theory.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Principles of risk mini- mization for learning theory

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:04.936376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bbac760a-b68a-4442-b36d-3155916c2683 · outbound

This paper cites Poincar´e differential privacy for hierarchy-aware graph embedding.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Poincar´e differential privacy for hierarchy-aware graph embedding

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:04.674242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c24f863e-5871-47dc-b3ec-4a83fd077171 · outbound

This paper cites Prompt- based unifying inference attack on graph neural networks.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Prompt- based unifying inference attack on graph neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:04.294898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f4ea5cd6-34f4-4457-82ab-778b3dd4a08c · outbound

This paper cites Sim- plifying graph convolutional networks.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Sim- plifying graph convolutional networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:04.085009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4c55ab6b-d3f7-4080-b0fc-5fc86a0fbaa5 · outbound

This paper cites Handling Distribution Shifts on Graphs: An Invariance Perspective.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Handling Distribution Shifts on Graphs: An Invariance Perspective

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:02.393129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 863e18b8-630e-4f88-8b45-b9404fec6d81 · outbound

This paper cites Graph con- trastive learning with augmentations.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Graph con- trastive learning with augmentations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:02.571136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba3d051c-833f-4196-9290-c9dcc6433736 · outbound

This paper cites Graph neural networks and their cur- rent applications in bioinformatics.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Graph neural networks and their cur- rent applications in bioinformatics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:03.874484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8da3b973-61e1-40ee-86e4-b594a8ef00eb · outbound

This paper cites Disen- tangled contrastive learning for fair graph representations.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Disen- tangled contrastive learning for fair graph representations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:03.668721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 39de275f-bea9-46a3-aa2c-d70290625b98 · outbound

This paper cites Graph Attention Networks.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Graph Attention Networks

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:01.819359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a971481d-438a-4056-b094-c96902b79760 · outbound

This paper cites A survey of graph neural net- works for social recommender systems.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks A survey of graph neural net- works for social recommender systems

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:06.145278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 05cdae6e-2213-4f46-9899-445a9481636f · outbound

This paper cites Deep fusion clustering network.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Deep fusion clustering network

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:05.320477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b0724fa9-3474-4abe-99b3-ee6b0a0ed7b8 · outbound

This paper cites Segmentations-leak: Membership in- ference attacks and defenses in semantic image segmen- tation.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Segmentations-leak: Membership in- ference attacks and defenses in semantic image segmen- tation

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:07.870614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c2d9d00-07b1-4f05-94a1-dc451d71bfa7 · outbound

This paper cites Graph neural networks for clinical risk prediction based on electronic health records: A survey.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Graph neural networks for clinical risk prediction based on electronic health records: A survey

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:08.362338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 339ff31c-5f8a-4d58-99df-b8223c1e8819 · outbound

This paper cites Recognition in terra incognita.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Recognition in terra incognita

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:08.601018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 99a967d8-bd8e-42bf-90ed-adb64d1cd760 · outbound

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

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Node-Level Membership Inference Attacks Against Graph Neural Networks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:59.529378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cc7666f-a938-4d2a-b985-74bd588f519c · outbound

This paper cites To Trust or Not To Trust Prediction Scores for Membership Inference Attacks.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks To Trust or Not To Trust Prediction Scores for Membership Inference Attacks

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:05:03.212799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1ee90d6f-db62-46c9-bacc-7c1f157a23f5 · outbound

This paper cites Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:07.146994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c7e746a4-9c69-48f0-80d5-4f785a8432c6 · outbound

This paper cites Hyperbolic Geometric Latent Diffusion Model for Graph Generation.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Hyperbolic Geometric Latent Diffusion Model for Graph Generation

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:05:03.463199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6c1c4694-932d-4332-a1b0-45d0ec47a859 · outbound

This paper cites Hyperbolic geometric graph representation learning for hierarchy-imbalance node classification.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks Hyperbolic geometric graph representation learning for hierarchy-imbalance node classification

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:08.118076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b67ab05f-93d3-438d-813b-e7045d3420d5 · outbound

This paper cites LOGAN: Membership Inference Attacks Against Generative Models.

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks LOGAN: Membership Inference Attacks Against Generative Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:59.258581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.