Paper Citation Record · LEDGER

Mitigating the Structural Bias in Graph Adversarial Defenses

As of 22 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2504.20848.

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

pith.paper-citation-record.v1
2504.20848 v1

Coverage vector

measured 40 of 40 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation 5675e575-1b76-4fdc-baeb-d1fd2303c1fc · outbound

This paper cites A comprehensive survey on graph neural networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses A comprehensive survey on graph neural networks,

Reference 1

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Observation b084d752-1c6f-479d-b5de-2d77531e6703 · outbound

This paper cites Graph neural networks: A review of methods and applications,.

Mitigating the Structural Bias in Graph Adversarial Defenses Graph neural networks: A review of methods and applications,

Reference 2

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Observation f28a11e8-42b1-4d1e-a2b4-ce17c1b3aaee · outbound

This paper cites Deep learning on graphs: A survey,.

Mitigating the Structural Bias in Graph Adversarial Defenses Deep learning on graphs: A survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.945670Z

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Observation 6f5e626f-838c-4686-9886-e806081df75b · outbound

This paper cites Graph Neural Network for Traffic Forecasting: A Survey.

Mitigating the Structural Bias in Graph Adversarial Defenses Graph Neural Network for Traffic Forecasting: A Survey

Reference 4

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Source-reported events for the cited work

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Observation 22905088-21af-439b-a70d-6e785c676979 · outbound

This paper cites Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies.

Mitigating the Structural Bias in Graph Adversarial Defenses Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies

Reference 5

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Observation b7e505ab-ea9a-41b4-9a58-67a503432977 · outbound

This paper cites Adversarial attacks and defenses in images, graphs and text: A review,.

Mitigating the Structural Bias in Graph Adversarial Defenses Adversarial attacks and defenses in images, graphs and text: A review,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.934414Z

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Observation 811c4a91-8e43-44f2-955e-68621d62629c · outbound

This paper cites A Survey of Adversarial Learning on Graphs.

Mitigating the Structural Bias in Graph Adversarial Defenses A Survey of Adversarial Learning on Graphs

Reference 7

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Observation 8c35f9f0-0415-40a2-b54b-4346fe46ca49 · outbound

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

Mitigating the Structural Bias in Graph Adversarial Defenses Adversarial attacks on neural networks for graph data,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.922954Z

Source-reported events for the cited work

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Observation 3837b1dd-8cc5-4a4e-a079-d1d95107a63c · outbound

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

Mitigating the Structural Bias in Graph Adversarial Defenses Adversarial examples for graph data: Deep insights into attack and defense,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.911540Z

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Observation 41160d92-b668-4c38-a1c4-d945f032f3f5 · outbound

This paper cites Adversarial attack on graph structured data,.

Mitigating the Structural Bias in Graph Adversarial Defenses Adversarial attack on graph structured data,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.899950Z

Source-reported events for the cited work

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Observation 6833c652-b089-4569-aa96-6a61f2c9da31 · outbound

This paper cites Fast Gradient Attack on Network Embedding.

Mitigating the Structural Bias in Graph Adversarial Defenses Fast Gradient Attack on Network Embedding

Reference 11

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Source-reported events for the cited work

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Observation f4c79cd9-66f3-443f-93ee-34466c37d965 · outbound

This paper cites Single node injection attack against graph neural networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Single node injection attack against graph neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.887596Z

Source-reported events for the cited work

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Observation 3d4cec8b-859b-4bad-be84-c65fe1683aab · outbound

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

Mitigating the Structural Bias in Graph Adversarial Defenses All you need is low (rank) defending against adversarial attacks on graphs,

Reference 13

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Observation b5eab0bb-3b0d-4cd6-9a5f-9365f822c459 · outbound

This paper cites Robust graph convolutional networks against adversarial attacks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Robust graph convolutional networks against adversarial attacks,

Reference 14

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Observation c8486d6c-9e99-43a1-a6e1-12d7f0e9a97f · outbound

This paper cites Understanding structural vulnerability in graph convolutional networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Understanding structural vulnerability in graph convolutional networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.857577Z

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Observation 18e2e82f-450e-459f-8b6d-bebac24db458 · outbound

This paper cites Batch virtual adversarial training for graph convolutional networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Batch virtual adversarial training for graph convolutional networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.847523Z

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Observation aa034663-f4ce-4071-b3e1-dd7cbcaca124 · outbound

This paper cites Towards locality- aware meta-learning of tail node embeddings on networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Towards locality- aware meta-learning of tail node embeddings on networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.836387Z

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Observation c173e031-166f-4eb0-85b9-2ed2b1c47e20 · outbound

This paper cites Investigating and mitigating degree-related biases in graph convoltuional networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Investigating and mitigating degree-related biases in graph convoltuional networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.825682Z

Source-reported events for the cited work

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Observation e7e5897a-18f0-44ee-9326-505566c7b6ba · outbound

This paper cites Tail-gnn: Tail-node graph neural networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Tail-gnn: Tail-node graph neural networks,

Reference 19

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Observation 3ffc1171-52e5-40c5-9142-15505b71a4f0 · outbound

This paper cites Lte4g: Long-tail experts for graph neural networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Lte4g: Long-tail experts for graph neural networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.803865Z

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Observation 333d6259-dbbe-4b14-9b68-856ae3d81ac1 · outbound

This paper cites Rawlsgcn: Towards rawlsian difference principle on graph convolutional network,.

Mitigating the Structural Bias in Graph Adversarial Defenses Rawlsgcn: Towards rawlsian difference principle on graph convolutional network,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.794639Z

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Observation f6472a65-ed5b-40ea-859c-1d5ac4cf3e7d · outbound

This paper cites On generalized degree fairness in graph neural networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses On generalized degree fairness in graph neural networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.784486Z

Source-reported events for the cited work

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Observation 8b647e45-ee1f-40a2-8fb0-344f5fe03aef · outbound

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

Mitigating the Structural Bias in Graph Adversarial Defenses Semi-supervised classification with graph convolutional networks,

Reference 23

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Observation edff1286-1719-42a6-a24d-788665d35a3c · outbound

This paper cites Inductive representation learning on large graphs,.

Mitigating the Structural Bias in Graph Adversarial Defenses Inductive representation learning on large graphs,

Reference 24

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Observation 86127692-505e-4cb5-960c-4c3aca69c31a · outbound

This paper cites Graph attention networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Graph attention networks,

Reference 25

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Observation ce999898-d205-4c33-80c8-c2566a300419 · outbound

This paper cites Simplifying graph convolutional networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Simplifying graph convolutional networks,

Reference 26

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Observation 806cf05a-b3af-4964-9423-6e1f50c7e55c · outbound

This paper cites Towards deeper graph neural networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Towards deeper graph neural networks,

Reference 27

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Source-reported events for the cited work

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Observation 03841d30-e9dc-4f7b-a83a-7e86eeb4bdbb · outbound

This paper cites DeeperGCN: All You Need to Train Deeper GCNs.

Mitigating the Structural Bias in Graph Adversarial Defenses DeeperGCN: All You Need to Train Deeper GCNs

Reference 28

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Observation 47e4ecc8-8048-4298-b8dd-d817dc0cb245 · outbound

This paper cites Scalable graph neural network training: The case for sampling,.

Mitigating the Structural Bias in Graph Adversarial Defenses Scalable graph neural network training: The case for sampling,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.718541Z

Source-reported events for the cited work

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Observation e47c18a8-d552-4010-a932-5631cb099cf1 · outbound

This paper cites Distgnn: Scalable distributed training for large-scale graph neural networks,.

Mitigating the Structural Bias in Graph Adversarial Defenses Distgnn: Scalable distributed training for large-scale graph neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.707167Z

Source-reported events for the cited work

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Observation f02f839a-6612-43f7-933d-ab9bed3a6c77 · outbound

This paper cites Scalable and efficient full-graph gnn training for large graphs,.

Mitigating the Structural Bias in Graph Adversarial Defenses Scalable and efficient full-graph gnn training for large graphs,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.693908Z

Source-reported events for the cited work

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Observation 5494f943-554d-4cdc-9da1-ca2f37671b26 · outbound

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

Mitigating the Structural Bias in Graph Adversarial Defenses Adversarial attacks on graph neural networks via meta learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.681353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b3554fdd-d62d-43e2-82ed-758331680a3a · outbound

This paper cites Adversarial attack on large scale graph,.

Mitigating the Structural Bias in Graph Adversarial Defenses Adversarial attack on large scale graph,

Reference 33

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Source-reported events for the cited work

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Observation cadc0b37-a826-4ec7-b322-bc4a48c97a3e · outbound

This paper cites Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach,.

Mitigating the Structural Bias in Graph Adversarial Defenses Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.670163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6128ee49-fdb1-403f-8ac2-f11910e1c4b9 · outbound

This paper cites Gani: Global attacks on graph neural networks via imperceptible node injections,.

Mitigating the Structural Bias in Graph Adversarial Defenses Gani: Global attacks on graph neural networks via imperceptible node injections,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.659697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 01eb7128-65e0-4970-8924-ced9d79631d4 · outbound

This paper cites Robust training of graph convolutional networks via latent perturbation,.

Mitigating the Structural Bias in Graph Adversarial Defenses Robust training of graph convolutional networks via latent perturbation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.648658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5684824c-9c42-4014-b076-7fda0929e63d · outbound

This paper cites Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective.

Mitigating the Structural Bias in Graph Adversarial Defenses Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Reference 37

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Observation 2616244f-5708-4769-92d4-59433aae5933 · outbound

This paper cites Certifiable robustness to graph perturbations,.

Mitigating the Structural Bias in Graph Adversarial Defenses Certifiable robustness to graph perturbations,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.638350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bdb8029e-2daf-4bc8-8345-237f630587b3 · outbound

This paper cites K-nearest neighbor,.

Mitigating the Structural Bias in Graph Adversarial Defenses K-nearest neighbor,

Reference 39

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Source-reported events for the cited work

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Observation 09e32d5c-bbff-44cd-aa85-50a744e534ef · outbound

This paper cites Collective classification in network data,.

Mitigating the Structural Bias in Graph Adversarial Defenses Collective classification in network data,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:18.620559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

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