Paper Citation Record · LEDGER

Graph Foundation Models: Concepts, Opportunities and Challenges

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2310.11829.

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

pith.paper-citation-record.v1
2310.11829 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 26 of 26 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:37:17.837276Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:08:56.262293Z

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 7c2b9748-b7c2-4a81-8098-d90a96bd4ecb · inbound

Retrieval-Augmented Generation with Graphs (GraphRAG) cites this paper.

Retrieval-Augmented Generation with Graphs (GraphRAG) Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 254

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T04:33:39.679774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89e765c8-2181-4430-aa4b-a8f835257a82 · inbound

OpenGT: A Comprehensive Benchmark For Graph Transformers cites this paper.

OpenGT: A Comprehensive Benchmark For Graph Transformers Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:37:17.837276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afab44a1-1e26-40b8-900d-afe9420f714e · inbound

Masked Language Models are Good Heterogeneous Graph Generalizers cites this paper.

Masked Language Models are Good Heterogeneous Graph Generalizers Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:14.422754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91e175a5-a57e-4241-897a-b80391cba1ba · inbound

GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations cites this paper.

GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:40.810245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b439068-00a7-408b-8e40-13829c15735d · inbound

Grounding Intelligence in Movement cites this paper.

Grounding Intelligence in Movement Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T20:25:27.533051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74bb63b0-7e86-45c4-a5ad-4f9de8f2ad59 · inbound

RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation cites this paper.

RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T11:19:53.551247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e185516-6d21-44a5-b667-a658ea7e00bc · inbound

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding cites this paper.

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T23:38:52.474515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2986a500-a07d-440a-a19b-00e1e0872553 · inbound

SkillGraph: Graph Foundation Priors for LLM Agent Tool Sequence Recommendation cites this paper.

SkillGraph: Graph Foundation Priors for LLM Agent Tool Sequence Recommendation Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T23:05:48.359021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f7fc68ab-aba9-4e3b-950c-b89c4b341f42 · inbound

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment cites this paper.

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:56:06.579568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3e73843b-7d81-4900-9fff-2194dc048e15 · inbound

Agentic AIs Are the Missing Paradigm for Out-of-Distribution Generalization in Foundation Models cites this paper.

Agentic AIs Are the Missing Paradigm for Out-of-Distribution Generalization in Foundation Models Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:16:07.042057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f5be73b-1161-418d-877f-5f1b6a085886 · 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 Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T04:52:17.399667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f9cafef4-85a7-4a34-9766-24b93ab2e276 · 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: Concepts, Opportunities and Challenges

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T06:42:26.040529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24863c59-abe1-463b-a42e-28f93eca2b88 · inbound

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs cites this paper.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:53:17.639640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8b68de1-6ef3-4c72-8fff-3e9a672bd2ad · inbound

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs cites this paper.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:59:50.862419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 326f40a6-caa7-4e06-becf-2a0db538fd52 · 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: Concepts, Opportunities and Challenges

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:42:46.638059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ed121658-a487-455b-8dd8-87f14b2fb1e8 · 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: Concepts, Opportunities and Challenges

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:46:26.696022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f97207c3-0769-49c3-b4af-1b6f10c0d747 · inbound

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models cites this paper.

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:46:56.998658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 282e7bea-9b13-4d6a-a5c2-14b28b6e5b99 · inbound

Handling Feature Heterogeneity with Learnable Graph Patches cites this paper.

Handling Feature Heterogeneity with Learnable Graph Patches Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:08:56.265145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 352555db-2b26-4a4e-acbb-b761f94e1254 · inbound

Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection cites this paper.

Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T06:54:21.427776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b84fdf2-2fde-40cf-b51e-bb007de13b15 · inbound

A Weisfeiler-Leman Characterization of Global-Attention Graph Transformers for Mixed-Integer Linear Programs cites this paper.

A Weisfeiler-Leman Characterization of Global-Attention Graph Transformers for Mixed-Integer Linear Programs Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T17:46:02.689616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d54fc9d6-6616-4f5b-a7ab-93ab72c9a914 · inbound

Semi-Supervised Text-Attributed Graph Distillation cites this paper.

Semi-Supervised Text-Attributed Graph Distillation Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T12:58:09.948057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27034b93-bed4-48be-8b48-981093e802d7 · inbound

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning cites this paper.

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-31T18:33:46.367116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 983ca1e3-30f8-4221-81b3-628c748d3204 · 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: Concepts, Opportunities and Challenges

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T21:59:22.157306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42829d10-22a3-4239-a952-68633a2c5e50 · 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: Concepts, Opportunities and Challenges

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T16:12:18.124746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23647916-8088-497a-9085-7fc2442ba9e7 · inbound

PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs cites this paper.

PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T13:43:49.613912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d75dc334-86e0-4d11-9fb1-0243173e09d7 · inbound

PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs cites this paper.

PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T00:11:45.032208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.