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Paper Citation Record · LEDGER

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2206.09112.

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

pith.paper-citation-record.v1
2206.09112 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 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 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:27:55.992141Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T22:04:00.547273Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 687da5b6-7422-4cc1-836e-39256ff1306a · inbound

Foresee and Act Ahead: Task Prediction and Pre-Scheduling Enabled Efficient Robotic Warehousing cites this paper.

Foresee and Act Ahead: Task Prediction and Pre-Scheduling Enabled Efficient Robotic Warehousing Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 21

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unresolved
no resolver link, observed 2026-08-11T19:45:29.737287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:45:29.737287Z digest=sha256:94848bb8d79d87d44d5b301b37737a343afef97c3782550b9d1b7f9376cce470

Observation fcad1a80-d095-4a4d-8756-d440f6bfb13f · inbound

FairTP: A Prolonged Fairness Framework for Traffic Prediction cites this paper.

FairTP: A Prolonged Fairness Framework for Traffic Prediction Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 22

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no resolver link, observed 2026-08-11T13:13:10.883074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:10.883074Z digest=sha256:d5311911cf3f26889436d5aee46d3edfc772ec38fd4cabf45cbfab13b0d997f5

Observation 662b205c-6f39-4d2d-8d70-a8ea00596ea0 · inbound

Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities cites this paper.

Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 54

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no resolver link, observed 2026-08-10T20:23:00.216439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:23:00.216439Z digest=sha256:2ccf87d5ea8469439c388b2c070bddb07876975c249c360cda98be139fd99164

Observation 50db612e-a5f7-4814-bec0-83430b891035 · inbound

Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts cites this paper.

Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 3

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unresolved
no resolver link, observed 2026-08-16T04:27:55.992141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:27:55.992141Z digest=sha256:755ec90290f3198dab85b1512d138b17f76d9012b7e15a5ef44e1766e50a2a66

Observation 3219deb4-fbed-4acd-bba5-0a2573f90135 · inbound

Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion cites this paper.

Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 44

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unresolved
no resolver link, observed 2026-08-06T11:05:12.883348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:05:12.883348Z digest=sha256:1eee94c88300cf3e8197441fe99be16a409a410f39c8038df724b9132f57f6a5

Observation 9f2a341b-6935-456b-9e76-63ab0ab673fa · inbound

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction cites this paper.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 49

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unresolved
no resolver link, observed 2026-08-15T17:41:23.229440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:41:23.229440Z digest=sha256:dbc43400159563253655da10a39675fba5975b6af368cf7a7fe6e9921963b132

Observation 835afc26-9730-4367-972a-20ceb7b75f77 · inbound

Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges cites this paper.

Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 69

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unresolved
no resolver link, observed 2026-08-04T12:39:06.532399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T12:39:06.532399Z digest=sha256:6eae6266601ec573adc01afa8e1f0dc6f9f1a200f2015c46386c700fd35f1656

Observation 201d456e-b45b-4fbf-8aa1-b8b968e99ec3 · inbound

Uniform Inductive Spatio-Temporal Kriging cites this paper.

Uniform Inductive Spatio-Temporal Kriging Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 28

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verified exact
arxiv_id, observed 2026-05-15T16:30:09.739766Z

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.

source=pdf_text observed=2026-05-15T16:28:01.222142Z digest=sha256:ff2b24d5205ae0ddd2a9cca3f3dea57a75166180bdd7073c4ece0b32a46cb20a

Observation f076bc54-7411-4780-ba4e-c1360d6fa2f2 · inbound

Latent-Mark: An Audio Watermark Robust to Neural Codec Compression cites this paper.

Latent-Mark: An Audio Watermark Robust to Neural Codec Compression Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 32

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unresolved
no resolver link, observed 2026-07-15T14:41:18.515733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T14:41:18.515733Z digest=sha256:016a56dc49332d6417a9080d280aefa4fe787b1f5b3a956bce511520e4a46e6d

Observation 3ce63ccb-dde2-4e6c-9311-34938ffce602 · inbound

AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting cites this paper.

AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 19

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verified exact
arxiv_id, observed 2026-05-11T19:41:09.151795Z

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.

source=pdf_text observed=2026-05-08T11:16:49.338637Z digest=sha256:bf68034758faca193b152b9f9f6ff083b9221f859b1e72d20289e5bf275cf140

Observation 68ff3f6c-2706-41dd-b80f-e5031bf650ef · inbound

TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting cites this paper.

TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 40

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verified exact
arxiv_id, observed 2026-05-12T02:06:15.284885Z

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.

source=pdf_text observed=2026-05-12T02:03:09.116351Z digest=sha256:d2cb91b65a06200edf1b3288998f6fa58cb52f2eb00cb5d3d33b26edb888fd50

Observation c9a61593-3f87-4063-bf9f-4c92e7b9c4d2 · inbound

ADMFormer: An Adaptive-Decomposition Transformer with Time-Varying Masked Spatial Attention for Traffic Forecasting cites this paper.

ADMFormer: An Adaptive-Decomposition Transformer with Time-Varying Masked Spatial Attention for Traffic Forecasting Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 3

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verified exact
arxiv_id, observed 2026-06-29T22:04:00.458810Z

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.

source=pdf_text observed=2026-06-29T21:58:37.536550Z digest=sha256:43089ce2914d26639a305c1611941de36549db4ee50a5d357ea3c2133db82a78

Observation 0c129e82-8bf1-4135-bd98-9fe2f024ff45 · inbound

PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting cites this paper.

PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 11

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verified exact
arxiv_id, observed 2026-06-29T22:04:00.548679Z

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.

source=pdf_text observed=2026-06-29T21:55:30.343816Z digest=sha256:188f2e8e6cf54f200b66b612214569a8cebab73bcad8061b6f1c00e121837e55

Observation ca8a9135-2b1f-4343-8385-834d54d03662 · inbound

A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning cites this paper.

A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 4

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metadata mismatch
arxiv_id, observed 2026-06-29T14:03:29.702686Z

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.

source=pdf_text observed=2026-06-29T13:55:31.399633Z digest=sha256:c6b22ed361a4efe34a9f7a989598d1d7ddc810b028fe8e6f39f99ecdc5fcae0c

Observation bbb3ed58-104b-4d65-9a35-d06e80e23b83 · inbound

Do We Really Need Adaptive Global Spatial Attention for Traffic Forecasting? cites this paper.

Do We Really Need Adaptive Global Spatial Attention for Traffic Forecasting? Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 23

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unresolved
no resolver link, observed 2026-07-15T05:55:25.279392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T05:55:25.279392Z digest=sha256:0dac56ca3ba9c03ad1e3e9cea59c4325aace22aed2918ec6db8ce54b04b1ee79