Paper Citation Record · LEDGER

A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2303.07275.

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

pith.paper-citation-record.v1
2303.07275 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:13:12.686680Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:16:02.734365Z

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 6ae5bf1a-d5ff-4e78-a614-7ad026a4a37f · inbound

Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? cites this paper.

Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges

Reference 516

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:12.686680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86066ab5-af5c-4b85-9aff-cdf05add4107 · inbound

SGPT: Few-Shot Prompt Tuning for Signed Graphs cites this paper.

SGPT: Few-Shot Prompt Tuning for Signed Graphs A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T18:08:00.339808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96466d5d-a98c-46c4-b68e-882ed4e24f03 · inbound

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding cites this paper.

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-02T23:38:55.662828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e49d1b12-e40c-4b27-983a-f29a44f14c2a · inbound

Unified Graph Prompt Learning via Low-Rank Graph Message Prompting cites this paper.

Unified Graph Prompt Learning via Low-Rank Graph Message Prompting A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:16:02.744044Z

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.