Paper Citation Record · LEDGER

A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.00485.

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

pith.paper-citation-record.v1
2403.00485 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:25:15.179588Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T10:01:13.650921Z

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 5cf0dae9-77a5-45f6-86a7-f28c2cd4b314 · inbound

Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey cites this paper.

Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

Reference 114

Resolution: unresolved; no resolver link, observed 2026-08-07T11:25:15.179588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d406d10b-7263-4c63-8d96-05f0725fecb9 · inbound

DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules cites this paper.

DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:14:43.297051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cad18af-d203-49cf-8c56-f55a9bfdd2e6 · inbound

Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints cites this paper.

Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:02:02.523309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 112613f4-f088-4955-a74a-3504ad01b22f · inbound

Superconductivity in atom-intercalated quaternary hydrides under ambient pressure cites this paper.

Superconductivity in atom-intercalated quaternary hydrides under ambient pressure A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T10:19:28.270396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90bd9264-7145-4ff7-87ea-4122460903cf · inbound

CrystalICL: Enabling In-Context Learning for Crystal Generation cites this paper.

CrystalICL: Enabling In-Context Learning for Crystal Generation A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T15:41:53.073523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25ab9c66-19a6-4ac7-bf79-af400adcd12c · inbound

PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling cites this paper.

PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:01:13.653649Z

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.