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Paper Citation Record · LEDGER

Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.10727.

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

pith.paper-citation-record.v1
2406.10727 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:36:36.359574Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:08:49.784031Z

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 ee8788a0-728d-429b-9275-a0c8b2c97cd2 · inbound

GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning cites this paper.

GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T14:36:36.359574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:36:36.359574Z digest=sha256:25b5697a1e388ea7b3a65142c84fc82bc3c81a14f3f632c0a593e9a2acab2def

Observation a443f57e-d255-4537-be11-d1f882361ed7 · inbound

When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach cites this paper.

When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-18T08:46:07.681687Z

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.

source=pdf_text observed=2026-05-18T08:45:51.992431Z digest=sha256:f5842ec69e660cfecbaf2d3f056684b9309ad37d6716f6843ca4c4930bcd83ef

Observation 6ce1fd7c-ccc4-43b0-b797-a67b4c366089 · inbound

On the Safety of Graph Representation Learning cites this paper.

On the Safety of Graph Representation Learning Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:21:07.082969Z

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.

source=pdf_text observed=2026-05-08T12:17:28.087347Z digest=sha256:bac56b3f32394bda20adf9328d2e7ab45d54880f51437ff214a4a9dd5a1f80f2

Observation 0b6554e8-88c9-475b-b2b3-b674444804dd · inbound

SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory cites this paper.

SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 84

Resolution
verified exact
arxiv_id, observed 2026-05-13T04:52:17.364570Z

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.

source=arxiv_source observed=2026-05-13T04:45:34.957298Z digest=sha256:23e39412fc3a93d454de38d1b3ff0e91bd9fd378f697a891f0b349887bb937b9

Observation 2618cfd1-2946-4006-96dd-e44613348cae · inbound

LLM Features Can Hurt GNNs: Concatenation Interference on Homophilous Graph Benchmarks cites this paper.

LLM Features Can Hurt GNNs: Concatenation Interference on Homophilous Graph Benchmarks Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-03T19:08:49.785774Z

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.

source=pdf_text observed=2026-06-27T02:12:05.069667Z digest=sha256:1813d45c1862171df26a2e79826a892672727596d994d535696f5755a1db34ad

Observation 9f8df8a5-2d19-424e-af26-e6bca8de2761 · inbound

Attacking Graph Foundation Models Through Their Shared Representation cites this paper.

Attacking Graph Foundation Models Through Their Shared Representation Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-01T15:05:39.623297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T15:05:39.623297Z digest=sha256:1e7e8109b1b6755ba4c45c49b2a66aa3be9115218473b3d48245310e5c0e6785

Observation 33a04f7a-11bb-4c0b-b5b3-e99ba88cd43a · inbound

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation cites this paper.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-01T13:29:51.975602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T13:29:51.975602Z digest=sha256:0d248394b74f3beb51dc8a60b9bd2c393a4adf4c5850f2c04571f213681badcd

Observation b3bb4989-ac69-4ff3-b76e-bdbd23e661e8 · inbound

GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks cites this paper.

GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-04T22:55:33.826338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:55:33.826338Z digest=sha256:0bb8c6c1df4eab4d92359112b8a1dc925206850ba5bd5c04dd01ef6922054270