Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 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 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:55:16.688055Z

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

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 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.

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 2026-08-08T06:32:00.761636+00:00.

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.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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.

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.

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.

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.