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Paper Citation Record · LEDGER

Modeling Multivariate Spatial-Temporal Data with Latent Low-Dimensional Dynamics

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2002.01305.

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

pith.paper-citation-record.v1
2002.01305 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:53:49.142542Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T07:23:07.014449Z

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 febfdd5b-8a2a-4cc5-92ac-e1e0d8b5927e · inbound

Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation cites this paper.

Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation Modeling Multivariate Spatial-Temporal Data with Latent Low-Dimensional Dynamics

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-08T04:53:49.142542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T04:53:49.142542Z digest=sha256:7f204956f125467e9df2d01d2290647afe740bf66141208cd225f6507877b975

Observation b1a169ff-b5dd-4289-b012-d6822e598eb4 · inbound

Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection cites this paper.

Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection Modeling Multivariate Spatial-Temporal Data with Latent Low-Dimensional Dynamics

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-20T07:23:07.015895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-20T07:20:43.847495Z digest=sha256:ffb5227cc64797731516f44613c202b31e21063c4a1c35a57f20c2434a17b3b7