Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2410.00385.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:47:03.319235Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-11T01:47:47.779051Z
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 2cbc79ad-de16-4696-b8f3-76d161ad1505 · inbound
FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting cites this paper.
FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:47:03.319235Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 457001d5-b2ee-4a5a-b617-19b4e38baf07 · inbound
Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting cites this paper.
Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:27:02.494301Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 95254c74-6a1d-4bf0-aeab-21524ffd0e7d · inbound
K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data cites this paper.
K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T07:41:00.998640Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation fce4206e-8335-428c-9977-a39bc13fc59a · inbound
K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data cites this paper.
K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T05:34:36.699746Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de9117eb-3035-480a-ad7c-2495314aab58 · inbound
Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth cites this paper.
Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T00:07:27.726093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f6cdf134-9067-43a9-b788-9d4845696d2c · inbound
STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting cites this paper.
STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-07-11T01:47:47.801242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c0764b98-4d83-4b54-8fb8-a5ca5ef7d614 · inbound
EMAGN: Efficient Multi-Attention Graph Network via Learned Clustering for Scalable Traffic Forecasting cites this paper.
EMAGN: Efficient Multi-Attention Graph Network via Learned Clustering for Scalable Traffic Forecasting STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T05:50:45.407188Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.