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Paper Citation Record · LEDGER

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

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 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 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:16:29.313042Z

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 20e4e27a-fd57-4ed0-898e-fddd2fd457b5 · inbound

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs cites this paper.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-12T05:35:47.308988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:35:47.308988Z digest=sha256:4711fda4973d3f79d7b6c40038454255920af22b94176dccb7e14b776d575d12

Observation 62a0e6e7-77ca-4e87-a369-1c7653121147 · inbound

Each Graph is a New Language: Graph Learning with LLMs cites this paper.

Each Graph is a New Language: Graph Learning with LLMs Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T18:18:17.677105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T18:18:17.677105Z digest=sha256:1131c4cc6fb2baa09f83c6389eae9cc85715d7286ef8bf43b03b7e9b4f53d31c

Observation 7e55411e-a9d5-45f0-8c14-7aeb1a30644f · inbound

GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model cites this paper.

GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-16T05:16:29.313042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:16:29.313042Z digest=sha256:f2cdd572090f2f47ce34e70c5356133479d85bf0aaa6ff54a8b9133051a58aec

Observation 09e26837-6d45-4c86-af9e-a4a4dc31f4ac · inbound

Homophily Enhanced Graph Domain Adaptation cites this paper.

Homophily Enhanced Graph Domain Adaptation Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-07T14:06:04.021216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:06:04.021216Z digest=sha256:fc3aafae78c8d559f3150d0771156b49f1be570674d17c30fdb8c75ec70c2050

Observation 7fc561c2-db69-444f-ac5d-2db1e0699ccb · inbound

H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs cites this paper.

H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T05:21:34.379679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:21:34.379679Z digest=sha256:2bd57420253c71058bf490a9f900841a84fec945a05e0f422cd46c6922daf300

Observation fdb416b1-b01c-4ab0-aaa0-f57f29b9d948 · inbound

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning cites this paper.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T22:42:26.613689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:42:26.613689Z digest=sha256:78027f48c153efdf0f3ed0df71386e8f1ab2999117221c31b11376e42e99070c

Observation bbf4a9bc-a171-48b3-9dac-a8220da6874b · inbound

PyG 2.0: Scalable Learning on Real World Graphs cites this paper.

PyG 2.0: Scalable Learning on Real World Graphs Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T15:03:07.306476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:03:07.306476Z digest=sha256:9ec615ec40fe96db9e2dd98bc25815c52a954a36040abf13f73b852756515a00

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=sha256:6372d1077b7da8f557e6625adcae5235866930ee394f5f066cf49e0929cbda3b

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-18T08:45:51.992431Z digest=sha256:dca6fef210777fb2ce65ea8218b993836cf2daf1d89f4bdde6c6c8facc274c71

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-08T12:17:28.087347Z digest=sha256:faa52f469045e80f80140ca021ce9c3ad7c06d521f9f503ea9e30e2b3845ed7a

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-13T04:45:34.957298Z digest=sha256:314a367e234ecbe4427e842d341391c5d819740ad50036619d0501910f4f8366

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-27T02:12:05.069667Z digest=sha256:39e0479492bb354c31bc4e1dfb9b171fe4d100261b405f303b10ac5dfde2729a

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:f2b130cc7b6b108c99e22dad22c9483982600795cf8b8ec17bdde402c4bf66a6

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=sha256:0ae04bd0611360aa83d242e67ba13dda0cb0c9ab4579a7c784535f137161a477

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=sha256:6d32c18e5573fb5a8a61b9cd6733fc31e645fc1085331d18ffd8e57352991f0e