Paper Citation Record · LEDGER

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 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 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:05:12.883348Z

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

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: 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 2026-08-10T06:31:04.303077+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: 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 2026-08-10T06:31:04.303077+00:00.

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

Resolution: 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 2026-08-10T06:31:04.303077+00:00.

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

Resolution: 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 2026-08-10T06:31:04.303077+00:00.

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

Resolution: 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 2026-08-10T06:31:04.303077+00:00.

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

Resolution: 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 2026-08-10T06:31:04.303077+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.