Paper Citation Record · LEDGER

Graph Foundation Models: A Comprehensive Survey

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2505.15116.

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

pith.paper-citation-record.v1
2505.15116 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:47:53.895750Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
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External citation measurements

2
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d9445f5c-9a89-4ffe-a770-b24a41642a97 · inbound

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

Turning Tabular Foundation Models into Graph Foundation Models Graph Foundation Models: A Comprehensive Survey

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-05T14:47:53.895750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cc0c169-f5e9-4eed-b4cf-84ce4c53082e · inbound

A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning cites this paper.

A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning Graph Foundation Models: A Comprehensive Survey

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:50:28.998814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2678112d-6648-4472-a45d-a0803d7dde13 · inbound

A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning cites this paper.

A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning Graph Foundation Models: A Comprehensive Survey

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T10:00:51.685962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53eeb16f-dad2-45dc-b904-770cf568d089 · inbound

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM cites this paper.

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM Graph Foundation Models: A Comprehensive Survey

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:45:57.431322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 52e5a5c5-22fa-4330-8c45-350e0401512b · inbound

Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models cites this paper.

Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models Graph Foundation Models: A Comprehensive Survey

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:01:11.973713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 42761da6-279c-4dd9-99cc-6928329edc6c · inbound

On the Safety of Graph Representation Learning cites this paper.

On the Safety of Graph Representation Learning Graph Foundation Models: A Comprehensive Survey

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:21:07.101844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0866a81e-64ca-4594-b511-9a3872eea6d4 · inbound

Structure-Centric Graph Foundation Model via Geometric Bases cites this paper.

Structure-Centric Graph Foundation Model via Geometric Bases Graph Foundation Models: A Comprehensive Survey

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:30.553918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 578d909a-f493-4ae7-a188-5e2727d4d414 · inbound

A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning cites this paper.

A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning Graph Foundation Models: A Comprehensive Survey

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:42:26.013613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ad958949-40e6-4d2b-b4ad-e3719c28ed4b · inbound

Deep Neural Sheaf Diffusion cites this paper.

Deep Neural Sheaf Diffusion Graph Foundation Models: A Comprehensive Survey

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:13:16.461708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f94cc82e-d410-4bae-8ca5-0580348e44b4 · inbound

Deep Neural Sheaf Diffusion cites this paper.

Deep Neural Sheaf Diffusion Graph Foundation Models: A Comprehensive Survey

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:25:00.119669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bc614796-4e01-4382-bd3b-e23832ac7923 · inbound

Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification cites this paper.

Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification Graph Foundation Models: A Comprehensive Survey

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:43:15.436680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4ebbd1ed-041e-4b87-bde2-01d637b37a25 · inbound

GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning cites this paper.

GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning Graph Foundation Models: A Comprehensive Survey

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:42:46.627404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0ff47bb9-5e18-46bd-9d9a-e8e9583a2fc4 · inbound

A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation cites this paper.

A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation Graph Foundation Models: A Comprehensive Survey

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:46:26.712782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e629c128-26f6-4b4f-bf5a-3a2cfa3b9c52 · inbound

OpenRFM: Dissecting Relational In-Context Learning cites this paper.

OpenRFM: Dissecting Relational In-Context Learning Graph Foundation Models: A Comprehensive Survey

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:06:41.439996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 025cc9f2-bbb4-4e5a-a8f3-ec8d505abf93 · inbound

The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning cites this paper.

The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning Graph Foundation Models: A Comprehensive Survey

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:56:55.662059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 601aa878-04a3-4b29-8954-5770e2a3b0fc · inbound

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems cites this paper.

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems Graph Foundation Models: A Comprehensive Survey

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:28:19.006691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e2f51f26-3ff3-4142-8884-ec7e4fe4080f · inbound

Canopy: A Heterograph Foundation Model for Metabolic Engineering cites this paper.

Canopy: A Heterograph Foundation Model for Metabolic Engineering Graph Foundation Models: A Comprehensive Survey

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.503962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f60906c2-6c9c-4b8e-9e7b-29a31828aa06 · 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 Graph Foundation Models: A Comprehensive Survey

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-01T00:53:53.532539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db924e7f-34f0-4a31-a6a3-5180cea912f5 · inbound

What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models cites this paper.

What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models Graph Foundation Models: A Comprehensive Survey

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-31T21:59:23.099471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24427022-1887-4c00-91bc-f942150839d8 · inbound

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models cites this paper.

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Graph Foundation Models: A Comprehensive Survey

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T16:12:20.521397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.