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Paper Citation Record · LEDGER

Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2405.13934.

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

pith.paper-citation-record.v1
2405.13934 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:48:05.669756Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:56:55.655532Z

Reference resolution

0 of 0 outbound references displayed

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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 251fc98d-2c88-4f8d-ad7c-fc9e40fbe42e · inbound

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs cites this paper.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 9

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no resolver link, observed 2026-08-11T05:23:48.030264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:23:48.030264Z digest=sha256:9933cebf665c094d54db141919de8560aeb2cbaf0df7fa559298cf8021bf6c18

Observation 4e36eca0-620c-4e39-9897-94fe2b4818c2 · inbound

DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach cites this paper.

DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 38

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no resolver link, observed 2026-08-10T14:40:13.237609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:40:13.237609Z digest=sha256:260356441e11b5a1a4225da87678f4f3780b267ee4aeff49dd86cc8a968d5169

Observation 50ed1f27-450e-49f5-9d6e-efb9c966ccf9 · inbound

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

SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 64

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no resolver link, observed 2026-08-08T19:28:52.768469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:28:52.768469Z digest=sha256:837768d98e01778fee36211c003e948e977307b745507fc916cbea21246cb0a1

Observation a787d89f-c41f-4e93-a526-f9850a79afca · inbound

GCoT: Chain-of-Thought Prompt Learning for Graphs cites this paper.

GCoT: Chain-of-Thought Prompt Learning for Graphs Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 64

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unresolved
no resolver link, observed 2026-08-08T10:55:16.688055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T10:55:16.688055Z digest=sha256:86410e60c53e2938df5c95234762060aebc65c63110cf0972f5a524f08dca8ef

Observation 7af9f09d-5243-4807-8400-f695f8a577f7 · inbound

Relation-Aware Graph Foundation Model cites this paper.

Relation-Aware Graph Foundation Model Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 44

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unresolved
no resolver link, observed 2026-08-15T20:48:05.669756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:48:05.669756Z digest=sha256:fbfdef1bd3c530b6f8b37c07645b57a5cb7e2ec0b39bc67c0bb01a2761febf2d

Observation 552c20b7-ce33-4612-bf9f-f9a114ebdfdb · inbound

GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks cites this paper.

GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-25T07:35:28.046074Z

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.

source=pdf_text observed=2026-05-25T07:34:42.234291Z digest=sha256:e25a2e11336a9efaf21a0c00606a4882b366f0592f85c874271b6dc0f3491d1c

Observation c8e94bb1-f054-405b-b149-f1174243db69 · 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 Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 55

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no resolver link, observed 2026-08-02T23:38:56.742968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:38:56.742968Z digest=sha256:02859c415a7f23649357b36d6abd0bdb38b260dcb6dbc6dcf065ae21913fce5a

Observation 49a5fdad-9308-4006-9665-0654aa313f2b · inbound

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

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 40

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T16:56:06.605107Z

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.

source=pdf_text observed=2026-05-09T14:30:55.764387Z digest=sha256:9af86f5739f7c470b5eda28dcc5ce4fb2cf5fe1a753a642f7dc0aa65f64f6905

Observation b8c9e397-dedc-4f00-b1d4-33bf16758023 · inbound

Bridging Input Feature Spaces Towards Graph Foundation Models cites this paper.

Bridging Input Feature Spaces Towards Graph Foundation Models Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 23

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metadata mismatch
arxiv_id, observed 2026-05-11T17:41:08.780958Z

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.

source=pdf_text observed=2026-05-08T17:18:21.259797Z digest=sha256:29af743ad8a45c149544ff3de657a3890ef93db22dd53a0bf194d4448f56a06b

Observation ed63c49f-aea0-4bd9-9dab-91d0acad6d5d · 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 Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 31

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metadata mismatch
arxiv_id, observed 2026-05-13T04:52:17.388697Z

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.

source=arxiv_source observed=2026-05-13T04:45:34.957298Z digest=sha256:c3ac7f45064c18be12c6229a931f349530fcd2d8ef0768176170186f65aac8f7

Observation a78616ed-2d9d-4787-a150-7ee487f90bcb · 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 Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 90

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metadata mismatch
arxiv_id, observed 2026-05-14T20:22:54.399341Z

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.

source=pdf_text observed=2026-05-14T20:22:43.876230Z digest=sha256:611d160a1f2ba0562db3e753545b0030009cce08d3d3a6c303ef4a4e1241cc8d

Observation f4b00e03-4595-48a6-a807-c865b89d431a · 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 Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T11:56:55.656953Z

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.

source=pdf_text observed=2026-06-28T02:40:03.713695Z digest=sha256:3a2e990ec61ea19987cc74341e787d6c9c0acac206a36c16c8c441c2301a0c50

Observation 755930fc-b15d-47a3-8da6-db92e80532c3 · 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 Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 48

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no resolver link, observed 2026-08-01T00:53:53.557621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:53:53.557621Z digest=sha256:ade2cb99fcdad8262801e03c30f360c6392dcff74d3ecc145ee84667d38c7e66

Observation 80bf6422-9a63-4f8f-a69f-fef15badf644 · inbound

AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control cites this paper.

AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 34

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no resolver link, observed 2026-08-15T15:29:03.534344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:29:03.534344Z digest=sha256:9193a85a50c5d92607ef679a56dce27999772ef7530fb99fd776f912a55c8bb4

Observation a241f6f4-925e-4511-8edd-8be6339c5d37 · 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 Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-31T21:59:23.119496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T21:59:23.119496Z digest=sha256:14508956a0db9ef5f92363100ea4a5d4dc5b924498d06b7ba54b633ea62a7cac

Observation 6967f1c8-d7a1-45c2-b754-0427d6d8ea89 · 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 Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 46

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unresolved
no resolver link, observed 2026-08-03T16:12:21.853589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:12:21.853589Z digest=sha256:bc3e0b45f2866830776e7b28cdef6a622d0ef7ccf03bd7e4571b50c3124b6040

Observation f5bdcad5-025f-4b00-87ba-62d8d5801e62 · inbound

Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts cites this paper.

Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 14

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unresolved
no resolver link, observed 2026-08-07T19:06:22.847595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:06:22.847595Z digest=sha256:d4d131bb9b2fbdcb4ccb1b1d943864c243405e454ceb1604ef54ffd648d2c908