Paper Citation Record · LEDGER

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning

As of 20 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2506.22510.

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

pith.paper-citation-record.v1
2506.22510 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:42:26.752908Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-14T20:22:43.876230Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T20:22:54.429975Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy22
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24fcf2a0-a44a-493a-9a55-78a64e0e8458 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning LLaMA: Open and Efficient Foundation Language Models

Reference 1

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Observation 0f398f0e-ee43-41e0-a859-85ee611fc300 · outbound

This paper cites All nlp tasks are generation tasks: A general pretraining framework.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning All nlp tasks are generation tasks: A general pretraining framework

Reference 2

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Observation 91e02ca8-3dd9-4f20-9e54-0cf4bcf8fdf6 · outbound

This paper cites GPT-4 Technical Report.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning GPT-4 Technical Report

Reference 3

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Observation e57bef61-566e-4640-98ee-10a189f79cad · outbound

This paper cites Understanding and improving visual prompting: A label-mapping perspective.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Understanding and improving visual prompting: A label-mapping perspective

Reference 4

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Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Unresolved cited work

Reference 5

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Observation 45f96d63-90c9-4e9d-81f6-4f2c7f0e2353 · outbound

This paper cites Imagenet-21k pretraining for the masses.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Imagenet-21k pretraining for the masses

Reference 6

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Observation 711f207b-6474-4412-b3e1-af66ed22ef5b · outbound

This paper cites Selectivity drives productivity: Efficient dataset pruning for enhanced transfer learning.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Selectivity drives productivity: Efficient dataset pruning for enhanced transfer learning

Reference 7

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Observation 72a2fb21-a6fc-4daa-a9ca-19ee88cf2aad · outbound

This paper cites Hhan: Comprehensive infectious disease source tracing via heterogeneous hypergraph neural network.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Hhan: Comprehensive infectious disease source tracing via heterogeneous hypergraph neural network

Reference 8

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Observation ef84e7b2-d076-4a7f-b8d8-a874420e1b9d · outbound

This paper cites Social recommendation via graph-level counterfactual augmentation.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Social recommendation via graph-level counterfactual augmentation

Reference 9

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Observation cfd59b90-18b3-4fdd-a80d-67a159afde9d · outbound

This paper cites Hypergraph convolutional network for user-oriented fairness in recommender systems.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Hypergraph convolutional network for user-oriented fairness in recommender systems

Reference 10

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Observation 2ab6519f-feb5-45e2-9c51-b1b8c9e91e64 · outbound

This paper cites Graph contrastive learning with augmentations.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Graph contrastive learning with augmentations

Reference 11

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Observation 378a2772-2449-4509-af03-ea80a841365e · outbound

This paper cites Graphprompt: Unifying pre-training and downstream tasks for graph neural networks.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Graphprompt: Unifying pre-training and downstream tasks for graph neural networks

Reference 12

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Observation cc1a8c3e-8728-4866-9598-4b1e305f2687 · outbound

This paper cites All in one: Multi-task prompt- ing for graph neural networks.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning All in one: Multi-task prompt- ing for graph neural networks

Reference 13

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Observation a3e7b678-324e-41e9-b613-c10230de34e7 · outbound

This paper cites Universal prompt tun- ing for graph neural networks.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Universal prompt tun- ing for graph neural networks

Reference 14

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Observation 3e12ee7b-8ede-4049-81cf-b9ecddbcfb04 · outbound

This paper cites Gppt: Graph pre-training and prompt tuning to generalize graph neural networks.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Gppt: Graph pre-training and prompt tuning to generalize graph neural networks

Reference 15

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Observation f10369c1-5e6f-448f-95f5-92067c1edd83 · outbound

This paper cites Simgrace: A simple framework for graph contrastive learning without data augmentation.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Simgrace: A simple framework for graph contrastive learning without data augmentation

Reference 16

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Observation 80fef6a3-4d15-402c-bd20-3215af5c2a03 · outbound

This paper cites Higpt: Heterogeneous graph language model.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Higpt: Heterogeneous graph language model

Reference 17

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Observation c6dbf751-68f6-4729-a142-ba296dee624d · outbound

This paper cites One for All: Towards Training One Graph Model for All Classification Tasks.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning One for All: Towards Training One Graph Model for All Classification Tasks

Reference 18

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This paper cites OpenGraph: Towards Open Graph Foundation Models.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning OpenGraph: Towards Open Graph Foundation Models

Reference 19

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This paper cites Augmenting low-resource text classification with graph-grounded pre-training and prompting.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Augmenting low-resource text classification with graph-grounded pre-training and prompting

Reference 20

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Observation e63ce4b7-51a1-4071-97d4-23a88aeba899 · outbound

This paper cites Learning on Large-scale Text-attributed Graphs via Variational Inference.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Learning on Large-scale Text-attributed Graphs via Variational Inference

Reference 21

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Observation f06af779-3ee3-4450-8625-18439df8f00a · outbound

This paper cites All in one and one for all: A simple yet effective method towards cross-domain graph pretraining.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning All in one and one for all: A simple yet effective method towards cross-domain graph pretraining

Reference 22

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Observation b4241a0e-b127-4294-a90e-d443b5a16639 · outbound

This paper cites SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation

Reference 23

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Observation 26f5b4ff-b15b-467b-80fa-dba8ecde62c9 · outbound

This paper cites Collective classification in network data.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Collective classification in network data

Reference 24

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Observation cbdb2961-b3e9-4b55-a9fb-1a8dcab896d9 · outbound

This paper cites Multi-scale attributed node embedding.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Multi-scale attributed node embedding

Reference 25

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This paper cites Revisiting semi-supervised learning with graph embeddings.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Revisiting semi-supervised learning with graph embeddings

Reference 26

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Observation 0dd01d43-888a-4103-b811-0aab5fa46c5a · outbound

This paper cites Pitfalls of graph neural network evaluation.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Pitfalls of graph neural network evaluation

Reference 27

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Observation 59a8ab37-1a85-4457-bd3c-7a2ff29f6126 · outbound

This paper cites Image-based recommendations on styles and substitutes.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Image-based recommendations on styles and substitutes

Reference 28

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 29

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Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Inductive representation learning on large graphs

Reference 30

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Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Graph Attention Networks

Reference 31

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Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Graph transformer networks

Reference 32

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Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Unresolved cited work

Reference 33

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Observation c4e9e54a-5a6d-4662-b91c-6ecc4fa2689b · outbound

This paper cites The pagerank citation ranking: Bringing order to the web.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning The pagerank citation ranking: Bringing order to the web

Reference 34

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This paper cites Beyond low-frequency information in graph convolutional networks.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Beyond low-frequency information in graph convolutional networks

Reference 35

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Observation c7e6d768-8d06-44c5-90e3-c3b23dc8d237 · outbound

This paper cites Is homophily a necessity for graph neural networks? Learning,Learning, Jun 2021.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Is homophily a necessity for graph neural networks? Learning,Learning, Jun 2021

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:27.342855Z

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Observation 5efb7658-ea89-4518-833d-7317fb9d4fe5 · outbound

This paper cites The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges

Reference 37

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Observation 7fdea63b-89cf-46bd-8b39-37a9a1342f01 · outbound

This paper cites A critical look at the evaluation of GNNs under heterophily: Are we really making progress?.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 38

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This paper cites Harnessing language model for cross-heterogeneity graph knowledge transfer.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Harnessing language model for cross-heterogeneity graph knowledge transfer

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:27.229081Z

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Observation fdb416b1-b01c-4ab0-aaa0-f57f29b9d948 · outbound

This paper cites Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 40

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Source-reported events for the cited work

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Observation 38d9e24a-e173-404f-a662-ee699e847024 · outbound

This paper cites Towards Foundation Models for Knowledge Graph Reasoning.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Towards Foundation Models for Knowledge Graph Reasoning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:26.663456Z

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Observation 9a2fd000-c8d1-447d-896b-68180e581621 · outbound

This paper cites Gft: Graph foundation model with transferable tree vocabulary.Advances in Neural Information Processing Systems, 37:107403–107443, 2024.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Gft: Graph foundation model with transferable tree vocabulary.Advances in Neural Information Processing Systems, 37:107403–107443, 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:27.112524Z

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Pith citing papers

Observation 73280774-c3da-4893-b98c-e83b90f7650b · inbound

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models cites this paper.

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:22:54.434176Z

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