Paper Citation Record · LEDGER

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2606.06293.

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pith.paper-citation-record.v1
2606.06293 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T02:58:52.293969Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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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

42 of 42 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57c5d3d0-480d-464c-82f0-8dc433cb0804 · outbound

This paper cites The graph neural network model,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis The graph neural network model,

Reference 1

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Observation eb40861f-efc6-404d-9579-64dfc70b1d98 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Relational inductive biases, deep learning, and graph networks

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-02T11:46:55.896827Z

Source-reported events for the cited work

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Observation 40085d9f-274f-43dc-b42b-55830eba8fd1 · outbound

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

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Semi-supervised classification with graph convolutional networks,

Reference 3

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Observation 8a9bd25d-552d-4b23-8a57-246fb4262505 · outbound

This paper cites Inductive representation learning on large graphs,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Inductive representation learning on large graphs,

Reference 4

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Observation ae7eab79-444c-4c36-bfb6-c90870c4515c · outbound

This paper cites Modeling polypharmacy side effects with graph convolutional networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Modeling polypharmacy side effects with graph convolutional networks,

Reference 5

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Observation c96bdcd6-352f-4444-93a5-597c85544b2c · outbound

This paper cites Graph convolutional neural networks for web-scale recommender systems,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Graph convolutional neural networks for web-scale recommender systems,

Reference 6

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Observation 65be6775-0a0c-42d3-a512-4fb6547b94b4 · outbound

This paper cites Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,

Reference 7

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Source-reported events for the cited work

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Observation fdaa1a16-e125-4f6c-87fe-8564792d8445 · outbound

This paper cites Intriguing properties of neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Intriguing properties of neural networks,

Reference 8

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Source-reported events for the cited work

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Observation 08f01a51-b668-4cdd-85f2-778b7f277413 · outbound

This paper cites Explaining and harnessing adversarial examples,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Explaining and harnessing adversarial examples,

Reference 9

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Observation 47b5ea68-8c8f-4f46-acb2-8fca1f7de996 · outbound

This paper cites Adversarial attacks on neural networks for graph data,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Adversarial attacks on neural networks for graph data,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

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Observation 5dc56a1d-9661-4ddd-8d9e-b04ca31410e0 · outbound

This paper cites Adversarial attacks on graph neural networks via meta learning,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Adversarial attacks on graph neural networks via meta learning,

Reference 11

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Observation 29cee604-8714-460b-9d6a-4cebb97e3fcd · outbound

This paper cites Adversarial examples on graph data: Deep insights into attack and defense,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Adversarial examples on graph data: Deep insights into attack and defense,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

Source-reported events for the cited work

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Observation 3b73bf0c-3d90-4813-9054-7853b96bc194 · outbound

This paper cites Adversarial attacks on graph classification via Bayesian optimisation,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Adversarial attacks on graph classification via Bayesian optimisation,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

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Observation 75b3f129-e121-4f3a-8434-4cb5a3cdcf82 · outbound

This paper cites Revisiting adversarial attacks on graph neural networks for graph classification,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Revisiting adversarial attacks on graph neural networks for graph classification,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

Source-reported events for the cited work

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Observation eb97e15d-f819-4043-a91e-9c2001221785 · outbound

This paper cites GNNGuard: Defending graph neural net- works against adversarial attacks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis GNNGuard: Defending graph neural net- works against adversarial attacks,

Reference 15

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Observation baf097a8-92ec-410e-9211-a598773a3b53 · outbound

This paper cites All you need is low (rank): Defending against adversarial attacks on graphs,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis All you need is low (rank): Defending against adversarial attacks on graphs,

Reference 16

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Observation 8a098ecd-491a-4fea-afc6-0a1c3c0a917f · outbound

This paper cites A causality-aligned structure rationalization scheme against adversarial biased perturbations for graph neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis A causality-aligned structure rationalization scheme against adversarial biased perturbations for graph neural networks,

Reference 17

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Source-reported events for the cited work

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Observation c6552631-3864-4d9c-b3e9-f4b3b06444bd · outbound

This paper cites Adversarial training for graph neural networks via graph subspace energy optimization,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Adversarial training for graph neural networks via graph subspace energy optimization,

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7189e351-4722-402e-8570-6479aadb2d96 · outbound

This paper cites The Vapnik-Chervonenkis dimension of graph and recursive neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis The Vapnik-Chervonenkis dimension of graph and recursive neural networks,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

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Observation 6a204f64-7ad0-47b1-a8f9-2ca90bb613de · outbound

This paper cites Learning theory can (sometimes) explain generalisation in graph neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Learning theory can (sometimes) explain generalisation in graph neural networks,

Reference 20

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Observation af858be6-a7cc-4e31-8a77-8c3658a0ebec · outbound

This paper cites Generalization and represen- tational limits of graph neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Generalization and represen- tational limits of graph neural networks,

Reference 21

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Source-reported events for the cited work

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Observation 9da708f2-5979-4fc7-a8f2-ca337408492d · outbound

This paper cites Generalization bounds for graph convolutional neural networks via Rademacher complexity.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Generalization bounds for graph convolutional neural networks via Rademacher complexity

Reference 22

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Source-reported events for the cited work

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Observation ba44d2fa-f82a-47a5-a90f-1ec45c9919aa · outbound

This paper cites Stability and generalization of graph convolutional neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Stability and generalization of graph convolutional neural networks,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

Source-reported events for the cited work

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Observation fcd6da1a-229a-4ec8-bd1d-e01dac4ad882 · outbound

This paper cites The generalization error of graph convolutional networks may enlarge with more layers,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis The generalization error of graph convolutional networks may enlarge with more layers,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

Source-reported events for the cited work

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Observation e877cb99-61b3-4430-a797-dfe4cd9e7b6b · outbound

This paper cites Graph neural tangent kernel: Fusing graph neural networks with graph kernels,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Graph neural tangent kernel: Fusing graph neural networks with graph kernels,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

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Observation 9093698e-f414-42eb-b3b5-612e6665c869 · outbound

This paper cites Simplified PAC-Bayesian margin bounds,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Simplified PAC-Bayesian margin bounds,

Reference 26

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Observation 9ab4493c-9a98-4dc6-80c1-8ce00f011fb3 · outbound

This paper cites PAC-Bayes & margins,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis PAC-Bayes & margins,

Reference 27

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Source-reported events for the cited work

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Observation c8c11fcb-e30e-41cc-be88-d8cf0146f516 · outbound

This paper cites Catoni,PAC-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Catoni,PAC-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning

Reference 28

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Observation 6647122e-9faa-49b3-bcec-abfdda3e9991 · outbound

This paper cites PAC-Bayes bounds with data dependent priors,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis PAC-Bayes bounds with data dependent priors,

Reference 29

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Observation f5db0292-d91c-4d61-ac99-45e36f3a2e4e · outbound

This paper cites Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data,

Reference 30

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Observation 6566bbae-ba60-434c-a8ee-eb5fd5ad1f90 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Spectrally-normalized margin bounds for neural networks,

Reference 31

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Observation 111c6d87-f5b3-4a7a-9ceb-235da15256d4 · outbound

This paper cites A PAC-Bayesian approach to spectrally-normalized margin bounds for neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis A PAC-Bayesian approach to spectrally-normalized margin bounds for neural networks,

Reference 32

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Source-reported events for the cited work

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Observation 197c256b-cc82-48aa-ab36-a4a7eaf201c9 · outbound

This paper cites A PAC-Bayesian approach to generalization bounds for graph neural networks,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis A PAC-Bayesian approach to generalization bounds for graph neural networks,

Reference 33

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Observation 98462212-f2b3-48e7-b246-f19cde4cd099 · outbound

This paper cites Generalization in graph neural networks: Improved PAC-Bayesian bounds on graph diffusion,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Generalization in graph neural networks: Improved PAC-Bayesian bounds on graph diffusion,

Reference 34

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Observation 2f408fa7-3789-4764-ac95-233a6a676fe7 · outbound

This paper cites Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9dec34f9-2754-490f-974c-ae568d073d44 · outbound

This paper cites A PAC-Bayes analysis of adversarial robustness,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis A PAC-Bayes analysis of adversarial robustness,

Reference 36

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Observation 238d3564-e4db-4cee-8d1f-200a7c951117 · outbound

This paper cites PAC-Bayesian spectrally-normalized bounds for adversarially robust generalization,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis PAC-Bayesian spectrally-normalized bounds for adversarially robust generalization,

Reference 37

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Source-reported events for the cited work

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Observation a9003edb-75cf-423a-bf33-1106b786d6cd · outbound

This paper cites PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:55.899885Z

Source-reported events for the cited work

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Observation 05dce1fe-ca48-4184-bd4e-69c347606e9e · outbound

This paper cites Towards a unified pac-bayesian framework for norm-based generalization bounds.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Towards a unified pac-bayesian framework for norm-based generalization bounds

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:55.902682Z

Source-reported events for the cited work

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Observation 8f84a057-7678-4a1e-810d-6784fdc667f5 · outbound

This paper cites Discriminative embeddings of latent variable models for structured data,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Discriminative embeddings of latent variable models for structured data,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

Source-reported events for the cited work

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Observation 4e8d67fe-a7ca-4d0a-b834-2e54f36783a1 · outbound

This paper cites Pardo,Statistical Inference Based on Divergence Measures.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Pardo,Statistical Inference Based on Divergence Measures

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

Source-reported events for the cited work

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Observation e3da2fcc-259c-42c0-abd3-5c4e063b0d1a · outbound

This paper cites Hanson-Wright inequality and sub- gaussian concentration,.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Hanson-Wright inequality and sub- gaussian concentration,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-28T02:58:52.293969Z

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