Paper Citation Record · LEDGER

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting

As of 22 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.27604.

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

pith.paper-citation-record.v1
2607.27604 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T04:54:15.566302Z

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 exact0
verified fuzzy0
unresolved33
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cbd5f1fa-f774-442a-8172-69686a04e80d · outbound

This paper cites Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.431302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b044d9e7-15c6-48d8-abee-9904209ceb7a · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.436340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4f12850-5996-4025-8ee6-93571f0539a3 · outbound

This paper cites Graph wavenet for deep spatial- temporal graph modeling.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Graph wavenet for deep spatial- temporal graph modeling

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.440750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a0147ea-465c-4d93-a053-7108dcaa29e2 · outbound

This paper cites Practical adversarial attacks on spatiotemporal traffic forecasting models.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Practical adversarial attacks on spatiotemporal traffic forecasting models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.446192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17d905e8-d2d1-4945-a6c5-6898e4177a2e · outbound

This paper cites Adversarial diffusion attacks on graph-based traffic prediction models.IEEE Internet of Things Journal, 11(1):1481–1495, 2023.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Adversarial diffusion attacks on graph-based traffic prediction models.IEEE Internet of Things Journal, 11(1):1481–1495, 2023

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.450738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c55b3e68-f287-4dd0-a7c3-369e00ab8856 · outbound

This paper cites Robust spatiotemporal traffic forecasting with reinforced dynamic adversarial training.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Robust spatiotemporal traffic forecasting with reinforced dynamic adversarial training

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.455290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f26460f3-ef57-4ffd-9d5e-2688364a6e61 · outbound

This paper cites Disentangling adversarial robustness and generalization.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Disentangling adversarial robustness and generalization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.459874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4b453c9-fb71-4725-811b-da96c6364366 · outbound

This paper cites Adversarial examples are not easily detected: Bypassing ten detection methods.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Adversarial examples are not easily detected: Bypassing ten detection methods

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.463862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4729918a-17c2-4d14-859f-e9427f2041b9 · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.467653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61f83a6c-7d08-40ad-b1b4-96852cf33cde · outbound

This paper cites Attention based spatial-temporal graph convolutional networks for traffic flow forecasting.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Attention based spatial-temporal graph convolutional networks for traffic flow forecasting

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.471448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 195fd678-bddc-4ca0-afa6-d195b3570860 · outbound

This paper cites Gman: A graph multi-attention network for traffic prediction.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Gman: A graph multi-attention network for traffic prediction

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.475564Z

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Observation e812ef6f-148b-4955-af8a-6cce9967ad48 · outbound

This paper cites Spatial-Temporal Transformer Networks for Traffic Flow Forecasting.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Spatial-Temporal Transformer Networks for Traffic Flow Forecasting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.479361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a6bb5b6-d57f-4f54-88c3-78469f06817b · outbound

This paper cites Spatio- temporal adaptive embedding makes vanilla transformer sota for traffic forecasting.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Spatio- temporal adaptive embedding makes vanilla transformer sota for traffic forecasting

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.483877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b44cae2a-1838-4ce1-b1d9-1354094708d0 · outbound

This paper cites Adaptive graph convolutional recurrent network for traffic forecasting.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Adaptive graph convolutional recurrent network for traffic forecasting

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.487907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4e38925-ebc4-40c2-a934-20f34d3974d3 · outbound

This paper cites Connecting the dots: Multivariate time series forecasting with graph neural networks.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Connecting the dots: Multivariate time series forecasting with graph neural networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.492156Z

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Unavailable: canonical work link unavailable.

Observation d892e187-979e-4bb4-b5ff-ba9f9257cd05 · outbound

This paper cites Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.496169Z

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Unavailable: canonical work link unavailable.

Observation b04b4664-eef5-43d6-8fbd-de297247dc7c · outbound

This paper cites Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.500310Z

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Unavailable: canonical work link unavailable.

Observation f8c9c8d8-dd7f-43af-a0e5-d9f7fba39291 · outbound

This paper cites Unist: A prompt-empowered universal model for urban spatio-temporal prediction.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Unist: A prompt-empowered universal model for urban spatio-temporal prediction

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.504252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c13d2c8e-6d23-4770-9272-cb9dfdaa14ac · outbound

This paper cites Spatially Focused Attack against Spatiotemporal Graph Neural Networks.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Spatially Focused Attack against Spatiotemporal Graph Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.508196Z

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Unavailable: canonical work link unavailable.

Observation f639f2a0-6325-4ab5-b6c2-1abfd37b43e3 · outbound

This paper cites Transferability in data poisoning attacks on spatiotemporal traffic forecasting models.Transportation Research Part C: Emerging Technologies, 2025.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Transferability in data poisoning attacks on spatiotemporal traffic forecasting models.Transportation Research Part C: Emerging Technologies, 2025

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.512249Z

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Unavailable: canonical work link unavailable.

Observation 3ee0a720-ceb4-4348-8fe9-e944792b987c · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Towards deep learning models resistant to adversarial attacks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.516515Z

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Observation 6ca011bb-e8a9-460b-9b17-bd80e86787aa · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Theoretically principled trade-off between robustness and accuracy

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.520234Z

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Observation d2d86790-3cf1-499f-a23c-15868ad248ce · outbound

This paper cites GNNGuard: Defending graph neural networks against adversarial attacks.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting GNNGuard: Defending graph neural networks against adversarial attacks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.523779Z

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Unavailable: canonical work link unavailable.

Observation db341f1f-959d-4072-98bf-a0b8c5a28091 · outbound

This paper cites Graph structure learning for robust graph neural networks.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Graph structure learning for robust graph neural networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.527708Z

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Unavailable: canonical work link unavailable.

Observation 9f791a2d-e947-4556-b911-5f409c3991c7 · outbound

This paper cites MagNet: A two-pronged defense against adversarial examples.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting MagNet: A two-pronged defense against adversarial examples

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.532132Z

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Unavailable: canonical work link unavailable.

Observation d29c03c6-66ee-48b2-8f41-b2d338c20662 · outbound

This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Feature squeezing: Detecting adversarial examples in deep neural networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.536481Z

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Unavailable: canonical work link unavailable.

Observation 3783698c-e394-4b63-8598-24b546dad208 · outbound

This paper cites Detecting errors and imputing missing data for single-loop surveillance systems.Transportation Research Record, 1855(1):160–167, 2003.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Detecting errors and imputing missing data for single-loop surveillance systems.Transportation Research Record, 1855(1):160–167, 2003

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.540910Z

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Observation 113d4717-b82c-4b52-95cd-02e77c6131fa · outbound

This paper cites an unresolved cited work.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.545321Z

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Unavailable: canonical work link unavailable.

Observation dad34628-6825-45ec-86f5-5adf9cfd632e · outbound

This paper cites Detection of electromagnetic interference attacks on sensor systems.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Detection of electromagnetic interference attacks on sensor systems

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.550237Z

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Observation 7eb4eaad-f7b7-44e6-bed4-10aa3329ae79 · outbound

This paper cites Trick or heat? manipulating critical temperature-based control systems using rectification attacks.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Trick or heat? manipulating critical temperature-based control systems using rectification attacks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.554654Z

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Observation 5c56e942-5b56-4c72-9c5e-c4bc90a9e6fb · outbound

This paper cites Ghost talk: Mitigating EMI signal injection attacks against analog sensors.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Ghost talk: Mitigating EMI signal injection attacks against analog sensors

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.558471Z

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Unavailable: canonical work link unavailable.

Observation 09ccc652-3197-4768-8b0b-ce663cb41d4f · outbound

This paper cites Morley Mao, and Henry X.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Morley Mao, and Henry X

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.562385Z

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Unavailable: canonical work link unavailable.

Observation 6517ca8f-6e21-49d7-8182-88607fa8babf · outbound

This paper cites Google maps hacks, 2020.

Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting Google maps hacks, 2020

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T04:54:15.566302Z

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Pith citing papers

No inbound Pith citation observations are available.