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Paper Citation Record · LEDGER

AnyGraph: Graph Foundation Model in the Wild

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2408.10700.

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

pith.paper-citation-record.v1
2408.10700 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:11:12.458637Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:09:57.543813Z

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 b5d00a26-edf3-462e-a609-01423de1939e · inbound

Progressive Scaling Visual Object Tracking cites this paper.

Progressive Scaling Visual Object Tracking AnyGraph: Graph Foundation Model in the Wild

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-07T14:11:12.458637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:11:12.458637Z digest=sha256:a0a401129f4afbdadac8334086768788e0d3ae4ee84f7a8f4e80398acf806d8c

Observation 2a995ac1-8e3d-49a4-a543-34351d6078a8 · 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 AnyGraph: Graph Foundation Model in the Wild

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T05:21:35.050298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:21:35.050298Z digest=sha256:89cb584f271d9303e56613a3126a4b4e87627cbfdec041c22d4b03e1eb01248c

Observation a57b62e3-ff3c-4bda-9128-1a933f025342 · 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 AnyGraph: Graph Foundation Model in the Wild

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T14:36:36.460904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:36:36.460904Z digest=sha256:72d38bfed94a3b9de1d43a59257438c59b42746e476941d560c2158975a04ce5

Observation 8c4369e1-9954-4a63-b310-704c7fa03a35 · inbound

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

No Need to Train Your RDB Foundation Model AnyGraph: Graph Foundation Model in the Wild

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-02T23:33:32.718367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:33:32.718367Z digest=sha256:666c0e4fffb40e0cf7aab9d790abe61aaa7aa5f37b4b4ed80416fa25f5c08d2c

Observation f069c268-b7fa-45c2-a26a-1e6f3f7a8a5b · inbound

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

Bridging Input Feature Spaces Towards Graph Foundation Models AnyGraph: Graph Foundation Model in the Wild

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T17:41:08.704886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T17:18:21.259797Z digest=sha256:90fd77a1d2013d191e1d6cde9d260b77034643ff08d9f724173b93ee64c3623d

Observation 70f4c9e0-b1d3-4acb-8cf0-50f49596a7a2 · inbound

On the Safety of Graph Representation Learning cites this paper.

On the Safety of Graph Representation Learning AnyGraph: Graph Foundation Model in the Wild

Reference 60

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T19:21:07.119026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T12:17:28.087347Z digest=sha256:852946026ebbf9cae041d68ac5d363a3478045019984b2e52951d864f98b2233

Observation ca36c7cb-dc2d-4099-b78e-acf615c40e49 · inbound

Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation cites this paper.

Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation AnyGraph: Graph Foundation Model in the Wild

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:11:15.325075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T02:10:57.066057Z digest=sha256:2ff84346707eab400cf1bcbfcd949656ec5f4046d0a501415aa46d4586185746

Observation 212c4f1d-52fe-46f5-902f-754e7a72c483 · inbound

When Do Graph Foundation Models Transfer? A Data-Centric Theory cites this paper.

When Do Graph Foundation Models Transfer? A Data-Centric Theory AnyGraph: Graph Foundation Model in the Wild

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:33:15.266752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-29T08:29:37.039306Z digest=sha256:15ba3953e54999e715fdfc15dab9514dc989f59b07dc7bc1022e861f32039faf

Observation 3d1bcb41-59ba-4abf-b7ca-a57ce823a7be · inbound

A Fair Evaluation of Graph Foundation Models for Node Property Prediction cites this paper.

A Fair Evaluation of Graph Foundation Models for Node Property Prediction AnyGraph: Graph Foundation Model in the Wild

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-07-04T16:09:57.545268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T00:50:39.622302Z digest=sha256:f5b5600cb5eb663cf70d01f2a34270dec06596ac48b5de34ed1b1fd0401d6d33

Observation bca15257-d7f8-401d-a7b9-c7b79ea8e25e · inbound

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning cites this paper.

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning AnyGraph: Graph Foundation Model in the Wild

Reference 232

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T09:45:40.601967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-07-01T06:10:26.634933Z digest=sha256:1c93c5e2fe1fac939a2f7fdebb45e4a5cb76a7149544a7f067017ded6e4e7dfc

Observation 8814d22b-29fe-4dc6-893a-623a318023da · inbound

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework cites this paper.

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework AnyGraph: Graph Foundation Model in the Wild

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T22:39:09.664742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T22:39:09.664742Z digest=sha256:c891f8c6be86aa3de256757208b554f19c88ea8892463b95557d9b5c355cc44b

Observation d51672c7-8eb6-4c23-9a18-d2d90a87d160 · inbound

CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer cites this paper.

CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer AnyGraph: Graph Foundation Model in the Wild

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-01T00:53:53.541358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:53:53.541358Z digest=sha256:50d222089b1b9372ee4811b4da592dcb539f520a9a714018c6f162d247322f47