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Paper Citation Record · LEDGER

A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2202.07893.

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

pith.paper-citation-record.v1
2202.07893 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:02:32.293362Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:08:56.318181Z

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 7f53e449-abb6-4646-bd40-993616153c28 · inbound

Subgraph-level Universal Prompt Tuning cites this paper.

Subgraph-level Universal Prompt Tuning A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-24T03:38:50.014025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-24T03:37:59.957481Z digest=sha256:04a2ad098c5b444fe17c797948552db5bca37edd2563225e3ddbc0bd17846f40

Observation fe82c18e-eb9a-4235-bcfd-1ca60e19a403 · inbound

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning cites this paper.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-12T15:02:32.293362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:02:32.293362Z digest=sha256:5ff4a746806d9ec2ec092edd610ef6a95658830fda77a0d56d4a4c39d02cae38

Observation 035d0c03-192d-4d37-b53b-5e2cd577bc83 · inbound

Heterogeneous Graph Pre-training Based Model for Secure and Efficient Prediction of Default Risk Propagation among Bond Issuers cites this paper.

Heterogeneous Graph Pre-training Based Model for Secure and Efficient Prediction of Default Risk Propagation among Bond Issuers A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-10T22:17:13.762499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:17:13.762499Z digest=sha256:79167ca412878fb7fe89c4d1967b01ce8c3b18afc65592219eee57a73fa598de

Observation 563581d0-380e-4e4e-81bc-25cec9187448 · inbound

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning cites this paper.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:51:09.440572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-09T14:36:29.620205Z digest=sha256:fe2febcfacf6f9ae9fb0a604c46cff6933aa97e787cd78518e4778de9309bef7

Observation 7a9bce5b-3965-4bef-b3da-df6fb59cb03d · inbound

Handling Feature Heterogeneity with Learnable Graph Patches cites this paper.

Handling Feature Heterogeneity with Learnable Graph Patches A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-07-03T20:08:56.320992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-27T01:36:45.977332Z digest=sha256:a554134ee83dc0d631269c30f28bc21c1618efde024756b79e621e6541103527