Pith. sign in

Paper Citation Record · LEDGER

AnyGraph: Graph Foundation Model in the Wild

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 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 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:36:36.460904Z

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 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:bf261c6c1a2db9f613b5ed9a6c2d345cd591d1623770cef0fa0c2e69cfce1910

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=sha256:0ec06a37efc77f4f2d6097de196c6d8998bad14441823d5bca95b4ed0b42f35a

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 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-08T17:18:21.259797Z digest=sha256:de8048994b9b1efd876fa38b222fa62f1bb60f2a72f701611bb649d5d14748bd

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 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-08T12:17:28.087347Z digest=sha256:2d78a2c88724416bf65af67ddd25a4c386a1b00a75411df667feb4c63f007f6d

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 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-12T02:10:57.066057Z digest=sha256:20f44e95cecd5a2796807429834f34989ac84adeabf7eb709565b3484d1ca798

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 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-06-29T08:29:37.039306Z digest=sha256:9af7bc9855c6b6b23272f895f780344cc33043ed2d185b0bedc58d7c74000c60

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 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-26T00:50:39.622302Z digest=sha256:78d7851f1c10d1a3a708a5055b6e7e3166ae04779cb801afcda6091dff50ac47

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 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-07-01T06:10:26.634933Z digest=sha256:dea92c0a6fd5ca4bfde4f237e1642ffdeaa53e021e70f718b2e5fa0e0eb7b81d

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:ea634016c410607250b4159e22a18b7b515e572a955ca50c4df2ce52407edf8f

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:df1b7f78e83022c1f37d1141f8f449208537c6fbef9a1394fa810f949c196a8b