Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:25:39.279631Z
Paper Citation Record · LEDGER
Graph Foundation Models: A Comprehensive Survey
As of 23 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 22 inbound Pith citation observations for arXiv:2505.15116.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:37:21.354774Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T02:28:24.338817Z
Reference resolution
100 of 299 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved100
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External citation measurements
2
pith, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation d6c96622-da38-46ef-9360-8763fcc971d1 · outbound
This paper cites Machine learning algorithms-a review.
Graph Foundation Models: A Comprehensive Survey Machine learning algorithms-a review
Reference 1
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 880237bb-36ed-444c-854c-095f7cf9d11a · outbound
This paper cites Feature engineering for machine learning and data analytics.
Graph Foundation Models: A Comprehensive Survey Feature engineering for machine learning and data analytics
Reference 2
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ba71234-9024-4e7c-9723-4f01d0ef8c1d · outbound
This paper cites A comparative study of training algorithms for supervised machine learning.
Graph Foundation Models: A Comprehensive Survey A comparative study of training algorithms for supervised machine learning
Reference 3
- Resolution
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Source-reported events for the cited work
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Observation 058c5307-9b13-4dd7-b9a5-0953cf7b728f · outbound
This paper cites Machine learning.
Graph Foundation Models: A Comprehensive Survey Machine learning
Reference 4
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 672152a2-2ff7-4d8b-b90b-a657137cec3f · outbound
This paper cites Deep learning.
Graph Foundation Models: A Comprehensive Survey Deep learning
Reference 5
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e5a3e376-f7cc-49ed-8983-8ddcb53a8a46 · outbound
This paper cites Deep residual learning for image recognition.
Graph Foundation Models: A Comprehensive Survey Deep residual learning for image recognition
Reference 6
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4c614fa9-a1bf-47bd-b1d2-b78c9256b7cc · outbound
This paper cites Long short-term memory.
Graph Foundation Models: A Comprehensive Survey Long short-term memory
Reference 7
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed7790ac-1507-4f88-803a-aff24f7af631 · outbound
This paper cites Empirical evaluation of gated recurrent neural networks on sequence modeling.
Graph Foundation Models: A Comprehensive Survey Empirical evaluation of gated recurrent neural networks on sequence modeling
Reference 8
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d76ea71d-262d-4595-9827-043d568627ac · outbound
This paper cites A comprehensive survey on transfer learning.
Graph Foundation Models: A Comprehensive Survey A comprehensive survey on transfer learning
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f7cd56ba-9457-4dfb-998a-c9c53857bac1 · outbound
This paper cites Self-supervised learning: Generative or contrastive.
Graph Foundation Models: A Comprehensive Survey Self-supervised learning: Generative or contrastive
Reference 10
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 194282a0-0262-423c-bbbf-fda3a06ae8fb · outbound
This paper cites On the opportunities and risks of foundation models.
Graph Foundation Models: A Comprehensive Survey On the opportunities and risks of foundation models
Reference 11
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation edd57c8e-692a-4aea-a5fb-e3fd8efbefb5 · outbound
This paper cites Scaling laws for neural language models.
Graph Foundation Models: A Comprehensive Survey Scaling laws for neural language models
Reference 12
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e1a8786-1749-40d2-928e-c39cb99847dd · outbound
This paper cites Scaling llm test-time compute optimally can be more effective than scaling model parameters.
Graph Foundation Models: A Comprehensive Survey Scaling llm test-time compute optimally can be more effective than scaling model parameters
Reference 13
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d0593c0-ab5a-4164-9ccc-7cde1aad0a14 · outbound
This paper cites Gpt-4 technical report.
Graph Foundation Models: A Comprehensive Survey Gpt-4 technical report
Reference 14
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 486db1ce-0427-401a-ae0f-98358aac589b · outbound
This paper cites A comprehensive survey on pretrained foundation models: A history from bert to chatgpt.
Graph Foundation Models: A Comprehensive Survey A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Reference 15
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5b78e7ac-2de5-4146-a2d6-7e29ab737689 · outbound
This paper cites Florence: A new foundation model for computer vision.
Graph Foundation Models: A Comprehensive Survey Florence: A new foundation model for computer vision
Reference 16
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7dc8a444-1f1a-41dc-b21e-d0f01b709447 · outbound
This paper cites Language model beats diffusion–tokenizer is key to visual generation.
Graph Foundation Models: A Comprehensive Survey Language model beats diffusion–tokenizer is key to visual generation
Reference 17
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7343c257-5659-4d07-8afa-e5d14cf5316c · outbound
This paper cites Sequential modeling enables scalable learning for large vision models.
Graph Foundation Models: A Comprehensive Survey Sequential modeling enables scalable learning for large vision models
Reference 18
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 880fb0ed-810a-4f29-a95f-a5f113452146 · outbound
This paper cites Kipf and Max Welling.
Graph Foundation Models: A Comprehensive Survey Kipf and Max Welling
Reference 19
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab8a91e9-f8b1-4e69-a29f-6d889b47b512 · outbound
This paper cites Graph attention networks.
Graph Foundation Models: A Comprehensive Survey Graph attention networks
Reference 20
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c69bd90d-a6c1-4f6d-b8e3-e7ff69b82ece · outbound
This paper cites Inductive representation learning on large graphs.
Graph Foundation Models: A Comprehensive Survey Inductive representation learning on large graphs
Reference 21
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c68f90fe-caf1-4876-ae5d-fdaaf1c032c9 · outbound
This paper cites One for all: Towards training one graph model for all classification tasks.
Graph Foundation Models: A Comprehensive Survey One for all: Towards training one graph model for all classification tasks
Reference 22
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 01e64873-59a0-478e-88a9-6ef53fccf4ac · outbound
This paper cites GFT: Graph foundation model with transferable tree vocabulary.
Graph Foundation Models: A Comprehensive Survey GFT: Graph foundation model with transferable tree vocabulary
Reference 23
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f21eb6c3-5e18-4e84-be22-2292691af0ea · outbound
This paper cites Zerog: Investigating cross-dataset zero-shot transferability in graphs.
Graph Foundation Models: A Comprehensive Survey Zerog: Investigating cross-dataset zero-shot transferability in graphs
Reference 24
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 01c59653-57ab-4bd9-99d3-2028f0807280 · outbound
This paper cites All in one and one for all: A simple yet effective method towards cross-domain graph pretraining.
Graph Foundation Models: A Comprehensive Survey All in one and one for all: A simple yet effective method towards cross-domain graph pretraining
Reference 25
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05ef17a4-faaa-400b-b965-b88bce01f8ce · outbound
This paper cites AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection.
Graph Foundation Models: A Comprehensive Survey AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
Reference 26
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f72dab7-fd82-4cf4-a0da-ed7644071fe9 · outbound
This paper cites An unified search and recommendation foundation model for cold-start scenario.
Graph Foundation Models: A Comprehensive Survey An unified search and recommendation foundation model for cold-start scenario
Reference 27
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 040e82fc-0789-4b87-a77f-bd1ed34d3ee6 · outbound
This paper cites Towards foundation models for knowledge graph reasoning.
Graph Foundation Models: A Comprehensive Survey Towards foundation models for knowledge graph reasoning
Reference 28
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation be0cabb8-209a-4555-93f8-d13b2cd493a8 · outbound
This paper cites A foundation model for zero-shot logical query reasoning.
Graph Foundation Models: A Comprehensive Survey A foundation model for zero-shot logical query reasoning
Reference 29
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6c893f9e-cb0f-439a-8c33-a243a43f7fea · outbound
This paper cites From molecules to materials: Pre-training large generalizable models for atomic property prediction.
Graph Foundation Models: A Comprehensive Survey From molecules to materials: Pre-training large generalizable models for atomic property prediction
Reference 30
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6411abd7-bfa3-44ce-80cb-0c2f1ef70afc · outbound
This paper cites Unimot: Unified molecule-text language model with discrete token representation.
Graph Foundation Models: A Comprehensive Survey Unimot: Unified molecule-text language model with discrete token representation
Reference 31
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87e77601-980e-40a5-bfb9-aa6ceab38c46 · outbound
This paper cites Graphwiz: An instruction-following language model for graph computational problems.
Graph Foundation Models: A Comprehensive Survey Graphwiz: An instruction-following language model for graph computational problems
Reference 32
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 635dd025-f87c-4f60-b31c-82974ec0f531 · outbound
This paper cites Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking.
Graph Foundation Models: A Comprehensive Survey Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking
Reference 33
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0060cf74-1e87-4b28-902f-ea4c013eac5f · outbound
This paper cites Graph foundation models: Concepts, opportunities and challenges.
Graph Foundation Models: A Comprehensive Survey Graph foundation models: Concepts, opportunities and challenges
Reference 34
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f93296b8-15f6-4be3-9b71-fde0ec9c2b2d · outbound
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Graph Foundation Models: A Comprehensive Survey A survey on self-supervised graph foundation models: Knowledge-based perspective
Reference 35
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16430e77-2a1e-4fe4-a039-4a881e0c553a · outbound
This paper cites Graph foundation models are already here.
Graph Foundation Models: A Comprehensive Survey Graph foundation models are already here
Reference 36
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b14f48dd-c865-4eea-9ed9-13a6ac0c7ab6 · outbound
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Graph Foundation Models: A Comprehensive Survey Towards graph foundation models: A transferability perspective
Reference 37
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c1292460-4da7-40a8-a9ae-f095464dca47 · outbound
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Graph Foundation Models: A Comprehensive Survey A survey of cross-domain graph learning: Progress and future directions
Reference 38
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 98bfa48d-c653-417f-b7f5-eeda0e4a0679 · outbound
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Graph Foundation Models: A Comprehensive Survey Graph foundation models for recommendation: A comprehensive survey
Reference 39
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4974ce26-842f-4d59-b5d6-92a2f3c70e48 · outbound
This paper cites Large language models on graphs: A comprehensive survey.
Graph Foundation Models: A Comprehensive Survey Large language models on graphs: A comprehensive survey
Reference 40
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 29ad4d5c-6853-4cff-a432-72ced52ca399 · outbound
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Graph Foundation Models: A Comprehensive Survey A survey of graph meets large language model: Progress and future directions
Reference 41
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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This paper cites Graph machine learning in the era of large language models (llms).
Graph Foundation Models: A Comprehensive Survey Graph machine learning in the era of large language models (llms)
Reference 42
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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Graph Foundation Models: A Comprehensive Survey A survey of large language models for graphs
Reference 43
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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Graph Foundation Models: A Comprehensive Survey Graph theory
Reference 44
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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Graph Foundation Models: A Comprehensive Survey An appraisal of some shortest-path algorithms
Reference 45
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d9d825f4-7f0f-415b-8c00-fb5072997428 · outbound
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Graph Foundation Models: A Comprehensive Survey A tutorial on spectral clustering
Reference 46
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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Graph Foundation Models: A Comprehensive Survey Graph kernels
Reference 47
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Source-reported events for the cited work
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Graph Foundation Models: A Comprehensive Survey Deepwalk: Online learning of social representations
Reference 48
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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Graph Foundation Models: A Comprehensive Survey node2vec: Scalable feature learning for networks
Reference 49
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6cdb5d32-9c59-4180-8c34-1085eb8f247d · outbound
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Graph Foundation Models: A Comprehensive Survey Line: Large-scale information network embedding
Reference 50
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b1d1896-7dba-4e85-934c-b93b270d3c88 · outbound
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Graph Foundation Models: A Comprehensive Survey A comprehensive survey on graph neural networks
Reference 51
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42e3768f-7fa2-48c7-a902-d6d2ee222bfb · outbound
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Graph Foundation Models: A Comprehensive Survey Neural message passing for quantum chemistry
Reference 52
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dcc4e033-aa13-4b77-861d-c0bd1c2f1b0d · outbound
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Graph Foundation Models: A Comprehensive Survey GOFA: A generative one-for-all model for joint graph language modeling
Reference 53
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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Graph Foundation Models: A Comprehensive Survey Graph self- supervised learning: A survey
Reference 54
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 633f1a6c-b217-402a-a749-0e5aef68e520 · outbound
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Graph Foundation Models: A Comprehensive Survey Attention is all you need
Reference 55
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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Graph Foundation Models: A Comprehensive Survey Language models are few-shot learners
Reference 56
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7be4a342-f0dc-4fed-8d3c-3fa628ff276b · outbound
This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.
Graph Foundation Models: A Comprehensive Survey Bert: Pre-training of deep bidirectional transformers for language understanding
Reference 57
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac5a462b-f6e9-4f86-9954-3b96acd633ca · outbound
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Graph Foundation Models: A Comprehensive Survey PaLM 2 Technical Report
Reference 58
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ca720174-1f5a-4a21-89b2-1f1c67bc377a · outbound
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Graph Foundation Models: A Comprehensive Survey Llama: Open and efficient foundation language models
Reference 59
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 02913c72-894c-4dd7-835c-4711c8108fac · outbound
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Graph Foundation Models: A Comprehensive Survey Learning transferable visual models from natural language supervision
Reference 60
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a471f7b8-46c2-40b0-96d8-196d12cb690c · outbound
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Graph Foundation Models: A Comprehensive Survey Zero-shot text-to-image generation
Reference 61
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 603576ef-d9ee-42fd-ba2e-2b36fc6ed408 · outbound
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Graph Foundation Models: A Comprehensive Survey Emergent abilities of large language models
Reference 62
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 315393c3-ae94-4622-842f-5646cba76bf6 · outbound
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Graph Foundation Models: A Comprehensive Survey Opengraph: Towards open graph foundation models
Reference 63
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0b04e82a-560f-45e8-8e1f-41b78ab28a9e · outbound
This paper cites Anygraph: Graph foundation model in the wild.
Graph Foundation Models: A Comprehensive Survey Anygraph: Graph foundation model in the wild
Reference 64
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Source-reported events for the cited work
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Observation 4fce54e0-d1c1-44db-a8f1-7006de6b2696 · outbound
This paper cites Samgpt: Text-free graph foundation model for multi-domain pre-training and cross-domain adaptation.
Graph Foundation Models: A Comprehensive Survey Samgpt: Text-free graph foundation model for multi-domain pre-training and cross-domain adaptation
Reference 65
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f035f232-55c5-4877-81f1-12ba3c1186ed · outbound
This paper cites Fully-inductive node classification on arbitrary graphs.
Graph Foundation Models: A Comprehensive Survey Fully-inductive node classification on arbitrary graphs
Reference 66
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0e47df8-3177-4240-a5ec-2abb197964ad · outbound
This paper cites Not too little, not too much: a theoretical analysis of graph (over) smoothing.
Graph Foundation Models: A Comprehensive Survey Not too little, not too much: a theoretical analysis of graph (over) smoothing
Reference 67
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1dff4147-bb27-48bc-b587-5f5c5dc610cc · outbound
This paper cites Understanding over-squashing and bottlenecks on graphs via curvature.
Graph Foundation Models: A Comprehensive Survey Understanding over-squashing and bottlenecks on graphs via curvature
Reference 68
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9e53b2bb-2eee-411a-a2a9-f153fdd664fa · outbound
This paper cites Is homophily a necessity for graph neural networks? In ICLR, 2022.
Graph Foundation Models: A Comprehensive Survey Is homophily a necessity for graph neural networks? In ICLR, 2022
Reference 69
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2821e478-76f3-436f-8f59-34859ce08898 · outbound
This paper cites Cross-domain graph data scaling: A showcase with diffusion models.
Graph Foundation Models: A Comprehensive Survey Cross-domain graph data scaling: A showcase with diffusion models
Reference 70
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cd77d7e3-c1f3-4e25-874d-0a2d37d4e3f3 · outbound
This paper cites Inductive graph alignment prompt: Bridging the gap between graph pre-training and inductive fine-tuning from spectral perspective.
Graph Foundation Models: A Comprehensive Survey Inductive graph alignment prompt: Bridging the gap between graph pre-training and inductive fine-tuning from spectral perspective
Reference 71
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf6c189b-e448-4c6a-bbf0-1bc52eeb1470 · outbound
This paper cites Edge prompt tuning for graph neural networks.
Graph Foundation Models: A Comprehensive Survey Edge prompt tuning for graph neural networks
Reference 72
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87eee09b-7b31-4c05-871a-faccd155e1c0 · outbound
This paper cites Mole-bert: Rethinking pre-training graph neural networks for molecules.
Graph Foundation Models: A Comprehensive Survey Mole-bert: Rethinking pre-training graph neural networks for molecules
Reference 73
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 25ff9e6b-50ee-48ba-b5fc-5a1b4e491ad2 · outbound
This paper cites Gppt: Graph pre-training and prompt tuning to generalize graph neural networks.
Graph Foundation Models: A Comprehensive Survey Gppt: Graph pre-training and prompt tuning to generalize graph neural networks
Reference 74
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05ce2bad-50be-4790-bc43-29aee4dd8882 · outbound
This paper cites Unigraph: Learning a cross-domain graph foundation model from natural language.
Graph Foundation Models: A Comprehensive Survey Unigraph: Learning a cross-domain graph foundation model from natural language
Reference 75
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 97128147-b87b-4183-9b7b-5b181c37a8ba · outbound
This paper cites Learning cross-task generalities across graphs via task-trees.
Graph Foundation Models: A Comprehensive Survey Learning cross-task generalities across graphs via task-trees
Reference 76
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 559ed81c-f98a-4ab5-86f7-921173668a99 · outbound
This paper cites Holographic node represen- tations: Pre-training task-agnostic node embeddings.
Graph Foundation Models: A Comprehensive Survey Holographic node represen- tations: Pre-training task-agnostic node embeddings
Reference 77
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8fe89e14-5a7d-4b69-92bc-7ffca9d9a255 · outbound
This paper cites Llaga: Large language and graph assistant.
Graph Foundation Models: A Comprehensive Survey Llaga: Large language and graph assistant
Reference 78
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 47655b28-ab78-4979-bd87-7816f4397aa1 · outbound
This paper cites Mopi-hfrs: A multi- objective personalized health-aware food recommendation system with llm-enhanced interpretation.
Graph Foundation Models: A Comprehensive Survey Mopi-hfrs: A multi- objective personalized health-aware food recommendation system with llm-enhanced interpretation
Reference 79
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2de9bbbe-3aae-4770-9c19-b12ff78165bf · outbound
This paper cites Graphbert: Bridging graph and text for malicious behavior detection on social media.
Graph Foundation Models: A Comprehensive Survey Graphbert: Bridging graph and text for malicious behavior detection on social media
Reference 80
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7a4bbb76-8397-48c3-857d-880f30eadad6 · outbound
This paper cites Subgraph pooling: Tackling negative transfer on graphs.
Graph Foundation Models: A Comprehensive Survey Subgraph pooling: Tackling negative transfer on graphs
Reference 81
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03de61c8-5b10-4c56-ad74-c7b2e9586120 · outbound
This paper cites A review: Knowledge reasoning over knowledge graph.
Graph Foundation Models: A Comprehensive Survey A review: Knowledge reasoning over knowledge graph
Reference 82
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 48af8c65-6d48-4e97-aa7e-55d7b451aa44 · outbound
This paper cites Ngqa: A nutritional graph question answering benchmark for personalized health-aware nutritional reasoning.
Graph Foundation Models: A Comprehensive Survey Ngqa: A nutritional graph question answering benchmark for personalized health-aware nutritional reasoning
Reference 83
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4c00679e-0732-4723-91a8-893270c26b5a · outbound
This paper cites Diet-odin: A novel framework for opioid misuse detection with interpretable dietary patterns.
Graph Foundation Models: A Comprehensive Survey Diet-odin: A novel framework for opioid misuse detection with interpretable dietary patterns
Reference 84
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 796330d9-002c-4c2b-95db-fe3ed3def44d · outbound
This paper cites A generalization of transformer networks to graphs.
Graph Foundation Models: A Comprehensive Survey A generalization of transformer networks to graphs
Reference 85
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation beb6ddca-82b1-4500-a654-5ec6c90bf794 · outbound
This paper cites Neural graph pattern machine.
Graph Foundation Models: A Comprehensive Survey Neural graph pattern machine
Reference 86
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a33af367-2c2a-4b6c-b4da-7839330c25a7 · outbound
This paper cites Recipe for a general, powerful, scalable graph transformer.
Graph Foundation Models: A Comprehensive Survey Recipe for a general, powerful, scalable graph transformer
Reference 87
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:25:38.177475Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41a4f45d-6511-4c98-a676-5282f11987ad · outbound
This paper cites Minimol: A parameter-efficient foundation model for molecular learning.
Graph Foundation Models: A Comprehensive Survey Minimol: A parameter-efficient foundation model for molecular learning
Reference 88
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9038dc9-14a6-4103-a45b-822321919a48 · outbound
This paper cites Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning.
Graph Foundation Models: A Comprehensive Survey Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning
Reference 89
- Resolution
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- no resolver link, observed 2026-08-07T15:25:38.347557Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cfa0b9e8-3f94-4ce4-aa41-23c505f78eb7 · outbound
This paper cites A survey of large language models.
Graph Foundation Models: A Comprehensive Survey A survey of large language models
Reference 90
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f60e1fed-29f3-482d-b83b-45ee92aca06e · outbound
This paper cites Llama 2: Open foundation and fine-tuned chat models.
Graph Foundation Models: A Comprehensive Survey Llama 2: Open foundation and fine-tuned chat models
Reference 91
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:25:38.540001Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f1ea3fbb-6cc5-4d2d-b917-0e57939b4797 · outbound
This paper cites The llama 3 herd of models.
Graph Foundation Models: A Comprehensive Survey The llama 3 herd of models
Reference 92
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43ba5df4-4c19-49a6-8371-696b10bfd9f5 · outbound
This paper cites Qwen technical report.
Graph Foundation Models: A Comprehensive Survey Qwen technical report
Reference 93
- Resolution
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- no resolver link, observed 2026-08-07T15:25:38.717414Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 643c0970-df8b-4d6d-9a2d-409b0cba8190 · outbound
This paper cites Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.
Graph Foundation Models: A Comprehensive Survey Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Reference 94
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ac58037-06af-4275-b68f-7bac4a1c9ad3 · outbound
This paper cites Understanding and modeling job marketplace with pretrained language models.
Graph Foundation Models: A Comprehensive Survey Understanding and modeling job marketplace with pretrained language models
Reference 95
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 44fd4df6-2bb6-415a-806d-1f7d354e1121 · outbound
This paper cites A Survey on In-context Learning.
Graph Foundation Models: A Comprehensive Survey A Survey on In-context Learning
Reference 96
- Resolution
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- no resolver link, observed 2026-08-07T15:25:38.913487Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4d4b9c7-0a10-47ce-8809-2f304016a202 · outbound
This paper cites Chain-of-thought prompting elicits reasoning in large language models.
Graph Foundation Models: A Comprehensive Survey Chain-of-thought prompting elicits reasoning in large language models
Reference 97
- Resolution
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- no resolver link, observed 2026-08-07T15:25:39.015166Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e05534a2-0a4f-4732-894a-a597fc13f1e4 · outbound
This paper cites Large language models are zero-shot reasoners.
Graph Foundation Models: A Comprehensive Survey Large language models are zero-shot reasoners
Reference 98
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ca564f39-1d83-4200-93e5-7563fb97b43f · outbound
This paper cites Langgfm: A large language model alone can be a powerful graph foundation model.
Graph Foundation Models: A Comprehensive Survey Langgfm: A large language model alone can be a powerful graph foundation model
Reference 99
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f7d9bdb7-b976-49aa-999c-cdce363bbe2f · outbound
This paper cites Beyond text: A deep dive into large language models’ ability on understanding graph data.
Graph Foundation Models: A Comprehensive Survey Beyond text: A deep dive into large language models’ ability on understanding graph data
Reference 100
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 2b5ea99c-5867-4cee-92e0-6d613fd55154 · inbound
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions cites this paper.
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Graph Foundation Models: A Comprehensive Survey
Reference 115
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
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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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ad958949-40e6-4d2b-b4ad-e3719c28ed4b · inbound
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Deep Neural Sheaf Diffusion Graph Foundation Models: A Comprehensive Survey
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
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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
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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
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 295d127f-ebb1-47a7-9025-363dca0e9df1 · inbound
Can Graph Learning Learn Circuits? cites this paper.
Can Graph Learning Learn Circuits? Graph Foundation Models: A Comprehensive Survey
Reference 21
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.