Paper Citation Record · LEDGER

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2502.14546.

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

pith.paper-citation-record.v1
2502.14546 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:35:07.837362Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b27fbe69-a9be-4ca5-9564-d52705d6d7db · inbound

Bridging Theory and Practice in Link Representation with Graph Neural Networks cites this paper.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T21:35:07.837362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac662e52-a736-4fea-8be2-f3465755d7d6 · inbound

Turning Tabular Foundation Models into Graph Foundation Models cites this paper.

Turning Tabular Foundation Models into Graph Foundation Models Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T14:47:53.844276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdfdbeab-1ac4-44b0-b486-00e02e9a926b · inbound

Artificial Intelligence for Food Innovation cites this paper.

Artificial Intelligence for Food Innovation Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:36:24.700846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5ebb885d-d58d-4971-b4e8-2987d807c34d · inbound

CrediBench: Building Web-Scale Network Datasets for Information Integrity cites this paper.

CrediBench: Building Web-Scale Network Datasets for Information Integrity Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:06.345517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4831cfa-c57c-42e6-bd6f-02f4d5a21856 · inbound

What Do Temporal Graph Learning Models Learn? cites this paper.

What Do Temporal Graph Learning Models Learn? Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:30.805401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9676c60e-d852-41a6-bbb7-b1cd06cc79fc · inbound

When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected cites this paper.

When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:20:11.831098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cb63f73f-fd05-4964-9956-f3d64f324a89 · inbound

No Need to Train Your RDB Foundation Model cites this paper.

No Need to Train Your RDB Foundation Model Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T23:33:30.749112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e8f914b-97a3-447a-8b42-2bc6214fb303 · inbound

Deep Neural Sheaf Diffusion cites this paper.

Deep Neural Sheaf Diffusion Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:13:16.464325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 69209d62-2991-4da6-a7a5-9cf14725c43b · inbound

Deep Neural Sheaf Diffusion cites this paper.

Deep Neural Sheaf Diffusion Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:25:00.131152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.