Paper Citation Record · LEDGER

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks

As of 12 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2505.14005.

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

pith.paper-citation-record.v1
2505.14005 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:49.781355Z

measured 38 of 38 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

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff9e9a91-0b93-4b3f-9a2f-95aff87cb610 · outbound

This paper cites Evaluating explainability for graph neural networks.Scientific Data, 10(1):144,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Evaluating explainability for graph neural networks.Scientific Data, 10(1):144,

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5adc241-91e5-41f3-9866-8436216d5bc4 · outbound

This paper cites the model to preserve more crucial structures within the explanation subgraphs, thereby boosting positive fidelity.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks the model to preserve more crucial structures within the explanation subgraphs, thereby boosting positive fidelity

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:50.265955Z

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Observation c7cb232c-9edf-4275-b83a-7f4c5caa1955 · outbound

This paper cites an unresolved cited work.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:42:50.459853Z

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Observation ba78e0b7-e2db-4b66-950e-7cb313fe61ef · outbound

This paper cites D4explainer: in-distribution gnn explana- tions via discrete denoising diffusion.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks D4explainer: in-distribution gnn explana- tions via discrete denoising diffusion

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.733925Z

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Observation e5b7a549-b9b6-4fe9-9cd9-dd5d5e057733 · outbound

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Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Unresolved cited work

Reference 7

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Observation 1411bf58-cb18-499c-afad-5799b3d74b97 · outbound

This paper cites Good: A graph out-of-distribution benchmark.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Good: A graph out-of-distribution benchmark

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.173594Z

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Observation 86118534-cd9f-48b8-9224-f3e9a53ec3c0 · outbound

This paper cites Mean-field theory of graph neural networks in graph partitioning.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Mean-field theory of graph neural networks in graph partitioning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.041190Z

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Observation 6d68baf3-a044-42d5-b904-3824d74f3f42 · outbound

This paper cites Distribution shift detection for the postmarket surveillance of medical ai algorithms: a retro- spective simulation study.NPJ Digital Medicine, 7(1):120,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Distribution shift detection for the postmarket surveillance of medical ai algorithms: a retro- spective simulation study.NPJ Digital Medicine, 7(1):120,

Reference 13

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Observation 3e269927-18a2-4e89-9363-d2621eafd72f · outbound

This paper cites Xgexplainer: Robust evaluation-based explanation for graph neural networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Xgexplainer: Robust evaluation-based explanation for graph neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:55.426920Z

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Observation dc24fc07-4a55-4534-9798-91e8dd39a15d · outbound

This paper cites Graph representation learning in biomedicine and healthcare.Nature Biomedical Engineering, 6(12):1353– 1369,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Graph representation learning in biomedicine and healthcare.Nature Biomedical Engineering, 6(12):1353– 1369,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:55.121921Z

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Observation a8bed3aa-d058-4064-8424-7b74de91a7f8 · outbound

This paper cites DIG: A turnkey library for diving into graph deep learning research.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks DIG: A turnkey library for diving into graph deep learning research

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:54.939042Z

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Observation 856cef43-5f6e-4e8f-8d61-2ce3ad9b04e7 · outbound

This paper cites Cf-gnnexplainer: Counterfactual explanations for graph neural networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Cf-gnnexplainer: Counterfactual explanations for graph neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:54.741762Z

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Observation e6ee0b65-6067-411d-a830-7c083de9670a · outbound

This paper cites Parameterized explainer for graph neural network.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Parameterized explainer for graph neural network

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:54.530780Z

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Observation f7165ccb-5a5d-4e25-8a4a-857e7ac36e8d · outbound

This paper cites Graph neural collaborative filtering with medical content-aware pre-training for treatment pattern recommendation.Pattern Recognition Letters, 185:210– 217,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Graph neural collaborative filtering with medical content-aware pre-training for treatment pattern recommendation.Pattern Recognition Letters, 185:210– 217,

Reference 20

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Observation 7476c316-8f34-4c06-ac8f-0cd19acd35d3 · outbound

This paper cites Pope, Soheil Kolouri, Moham- mad Rostami, Charles E.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Pope, Soheil Kolouri, Moham- mad Rostami, Charles E

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:53.906720Z

Source-reported events for the cited work

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Observation 5e6d22ea-d2f6-4b77-b123-1c578c41ee8a · outbound

This paper cites Generating Robust Counterfactual Witnesses for Graph Neural Networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Generating Robust Counterfactual Witnesses for Graph Neural Networks

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:42:50.116039Z

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Observation 9b85fe0f-ad01-4ae2-81c4-7026c31b136b · outbound

This paper cites Graphvae: Towards generation of small graphs using variational autoencoders.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Graphvae: Towards generation of small graphs using variational autoencoders

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:53.643001Z

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Observation fea3a0d6-7f6f-4fc3-8078-557d5babee44 · outbound

This paper cites Wasserstein weisfeiler-lehman graph kernels.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Wasserstein weisfeiler-lehman graph kernels

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:53.465267Z

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Observation 39758b3c-3328-4c6e-b928-68f59b60d365 · outbound

This paper cites Graph attention networks.stat, 1050(20):10–48550,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Graph attention networks.stat, 1050(20):10–48550,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:53.247095Z

Source-reported events for the cited work

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Observation 22073dff-5102-497c-a33f-ccdc91fe5226 · outbound

This paper cites Pgm-explainer: Probabilistic graphical model explanations for graph neural networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Pgm-explainer: Probabilistic graphical model explanations for graph neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:53.033635Z

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Observation 52985295-7b0e-4848-950a-4f5e753ce453 · outbound

This paper cites Gn- ninterpreter: A probabilistic generative model-level expla- nation for graph neural networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Gn- ninterpreter: A probabilistic generative model-level expla- nation for graph neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:52.691136Z

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Observation d9caacbc-6174-4732-b01e-0cba06a5c7cc · outbound

This paper cites Discovering invariant rationales for graph neural networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Discovering invariant rationales for graph neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:52.463816Z

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Observation cf03edfb-1ce2-4b7a-9909-83d07149266d · outbound

This paper cites Heterogeneous graph knowledge enhanced stock market prediction.AI Open, 2:168–174,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Heterogeneous graph knowledge enhanced stock market prediction.AI Open, 2:168–174,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:52.218890Z

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Observation 4af47348-af29-42df-81fb-149bfd3b6fd4 · outbound

This paper cites Adaptive hypergraph network for trust prediction.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Adaptive hypergraph network for trust prediction

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:51.921546Z

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Observation d72b8c86-3d02-4d56-b5d6-5c0ede7f87c2 · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Gnnexplainer: Generating explanations for graph neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:51.699210Z

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Observation 5614de41-d6ee-45e8-8be4-7bd31d70fa3a · outbound

This paper cites Xgnn: Towards model-level explanations of graph neural networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Xgnn: Towards model-level explanations of graph neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:51.427196Z

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Observation 03fde2a3-f3c2-4c39-a8cc-10930b46cbc0 · outbound

This paper cites Research on graph neural network in stock market.Procedia Computer Science, 214:786–792,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Research on graph neural network in stock market.Procedia Computer Science, 214:786–792,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:51.222058Z

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Observation 935bc60f-3ae7-4c00-8ce1-929d1ebecb11 · outbound

This paper cites Mixupexplainer: Generalizing explanations for graph neural networks with data augmentation.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Mixupexplainer: Generalizing explanations for graph neural networks with data augmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:51.052441Z

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Observation bb3c4bc3-1910-4bf6-9715-5f43a814cd8c · outbound

This paper cites Domain disentanglement with interpolative data aug- mentation for dual-target cross-domain recommendation.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Domain disentanglement with interpolative data aug- mentation for dual-target cross-domain recommendation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:50.798423Z

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Observation 46fbd5b2-1224-47db-b31b-38d0ec707285 · outbound

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Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:42:50.650114Z

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Observation 2663186a-1e4a-4bb5-ad95-b2766998a5eb · outbound

This paper cites Hidden in plain sight: Subgroup shifts escape ood detection.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Hidden in plain sight: Subgroup shifts escape ood detection

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:55.773599Z

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Observation 4a1cd1da-5755-4c6c-9762-50b1bf030489 · outbound

This paper cites Auto-encoding variational bayes.stat, 1050:1,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Auto-encoding variational bayes.stat, 1050:1,

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:55.910272Z

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Observation baf62fbc-f8e9-4efd-bec5-51daf9701a68 · outbound

This paper cites Deepnote-gnn: predicting hospital readmission using clin- ical notes and patient network.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Deepnote-gnn: predicting hospital readmission using clin- ical notes and patient network

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.336286Z

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Observation 042c0891-74ca-4955-bb16-3cd37a0ecf27 · outbound

This paper cites Adversarial learning targeting deep neural network classification: A comprehensive review of defenses against attacks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Adversarial learning targeting deep neural network classification: A comprehensive review of defenses against attacks

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:54.329601Z

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Observation 8bd7df21-6bfd-4341-b9d3-00bc59cfac9c · outbound

This paper cites Graphframex: Towards systematic evaluation of explainability methods for graph neural networks.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Graphframex: Towards systematic evaluation of explainability methods for graph neural networks

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:57.132460Z

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Observation dac57acf-e0a5-4310-8f8e-98d8d476f8d5 · outbound

This paper cites Learning causally invariant representations for out-of-distribution generalization on graphs.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Learning causally invariant representations for out-of-distribution generalization on graphs

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.902848Z

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Observation 2e0be9fc-0b50-4f34-bd9c-01cf0ac57f58 · outbound

This paper cites Invariance principle meets information bottleneck for out-of-distribution gener- alization.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Invariance principle meets information bottleneck for out-of-distribution gener- alization

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:57.401613Z

Source-reported events for the cited work

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Observation 373072b4-85f0-47da-8adc-0f3ffc710b31 · outbound

This paper cites Few-shot causal repre- sentation learning for out-of-distribution generalization on heterogeneous graphs.TKDE, 37(4):1804–1818,.

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Few-shot causal repre- sentation learning for out-of-distribution generalization on heterogeneous graphs.TKDE, 37(4):1804–1818,

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.613506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

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