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Paper Citation Record · LEDGER

Universal Prompt Tuning for Graph Neural Networks

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.15240.

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

pith.paper-citation-record.v1
2209.15240 v5

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 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:40:51.588945Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:06:26.759029Z

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 26e49f25-fa80-46cb-9746-39154f3d3e1c · inbound

Vision Graph Prompting via Semantic Low-Rank Decomposition cites this paper.

Vision Graph Prompting via Semantic Low-Rank Decomposition Universal Prompt Tuning for Graph Neural Networks

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:51.588945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:40:51.588945Z digest=sha256:9f25ea22efaf874f1a3f6f85362f23eaedbda5fd6bb039217c4b61735687bc8c

Observation 34f38073-97b8-4e0e-8c4a-d5f399364221 · inbound

Efficient Prompt Learning for Traffic Forecasting cites this paper.

Efficient Prompt Learning for Traffic Forecasting Universal Prompt Tuning for Graph Neural Networks

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:06:26.763653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-12T01:20:37.192907Z digest=sha256:4881a1f9be2867b057aa40c543aaff5dd4ca2c4b47ff4f7b8da65bf61911011e