Paper Citation Record · LEDGER

Fully-inductive Node Classification on Arbitrary Graphs

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.20445.

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

pith.paper-citation-record.v1
2405.20445 v5

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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:27:45.644781Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:46:26.704154Z

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 a8f9637b-e626-43dd-b717-4d7796a984bb · inbound

RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry cites this paper.

RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry Fully-inductive Node Classification on Arbitrary Graphs

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-09T05:27:45.644781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6db9ee2-0292-4710-82af-95f60a560da4 · inbound

GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning cites this paper.

GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning Fully-inductive Node Classification on Arbitrary Graphs

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:27:31.624559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 390bb1f4-9b56-4bcc-9fdf-306586703766 · inbound

Bridging Input Feature Spaces Towards Graph Foundation Models cites this paper.

Bridging Input Feature Spaces Towards Graph Foundation Models Fully-inductive Node Classification on Arbitrary Graphs

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:41:08.786505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 916627ac-98fb-4cc0-8019-15d4c2a7ea5a · inbound

A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation cites this paper.

A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation Fully-inductive Node Classification on Arbitrary Graphs

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:46:26.706216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 65f9f0c4-33b0-4769-a56b-4e93564a9477 · inbound

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models cites this paper.

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Fully-inductive Node Classification on Arbitrary Graphs

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T16:12:22.241005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.