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Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 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 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:28:52.768469Z

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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  • verified fuzzy0
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  • malformed identifier0
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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 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

Resolution
unresolved
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=sha256:16e305dd038adafc4368d0874622bba174507ce04de87ff97ab53fb0424463e9

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

Resolution
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=sha256:6b56295eb20b349163ee9056987346d7b6d172edd5678c9b3ad0bc85666e970c

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-25T07:34:42.234291Z digest=sha256:78e49931624ebb3ded1f3785deeedfee32f189943a45f6333629a4f7b0ff5c33

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

Resolution
unresolved
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=sha256:422a3c30eb1016986952c373ca9a0c4ce6595ea91f0feff33fc74aff1c508700

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T14:30:55.764387Z digest=sha256:a80eeb4dbf1d5591ed13294eccbcfebb77326198b9cee0fdebf0d940265384e8

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T17:18:21.259797Z digest=sha256:409dcba4905d8fba1a7a317560d4f24810fa36dde9652b2905b4f36d9b9357be

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T04:45:34.957298Z digest=sha256:4ae2f64e988af3f07f36354ed3df743554cf3023737240cf1944ce4c428b55fc

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T20:22:43.876230Z digest=sha256:81d8615f5ed1dbf07e71d13f9d4d4c5804327db30f55ff3757e4ad7b391d133e

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T02:40:03.713695Z digest=sha256:774ac02633b546a7463bcf90683ebf1bfe44a1d467d5ed6c7b58d5d1999f67ee

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

Resolution
unresolved
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:e17796c572c6353d88c5493dd7980bcaaee3db2eb525f1e2130eadae59d3c1e9

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:bfaaf1e84b16c31ebcd61d0bd08194b2f0c1d42930df79eb5f4e4e5533298998

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

Resolution
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:d3e947c3a5e76048be8ac42ba7424ca54a8f7679ad8f026b379e5adf4eb12139

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

Resolution
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:d99a133b32b801c4ef84aa531204f2b4c5550083c0d97ce599c9510fa40fa759