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Paper Citation Record · LEDGER

Recurrent Multi-Graph Neural Networks for Travel Cost Prediction

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1811.05157.

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

pith.paper-citation-record.v1
1811.05157 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 1 of 1 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:43:29.358809Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T16:43:30.075637Z

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 bfb73537-d5c3-4632-98e0-b70c62140168 · inbound

AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks cites this paper.

AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks Recurrent Multi-Graph Neural Networks for Travel Cost Prediction

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-10T16:43:30.090762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-10T16:43:29.358809Z digest=sha256:f02edacc650652e1c65bd6a2b4bbb7ba8959a8a6d71b84124a0d193ab658e14c