Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2007.03113.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T20:23:00.052344Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T04:52:34.297907Z
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 c9c2a78a-83c4-4f35-b717-0d77a5aeeb95 · inbound
Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities cites this paper.
Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:23:00.052344Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 349513de-5e21-4e37-869b-c2d04ea92dba · inbound
Leveraging graph neural networks and mobility data for COVID-19 forecasting cites this paper.
Leveraging graph neural networks and mobility data for COVID-19 forecasting Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T04:52:34.300479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d6496f97-0933-4637-a85c-622b19e1b645 · inbound
ReInc: Scaling Training of Dynamic Graph Neural Networks cites this paper.
ReInc: Scaling Training of Dynamic Graph Neural Networks Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:28:16.817907Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3750f4aa-066e-4a3f-9324-585c166fac85 · inbound
Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting cites this paper.
Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:17:15.564174Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 09451b2d-cb47-4d63-ba31-8606571debab · inbound
Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approach cites this paper.
Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approach Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:48:09.997262Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.