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Paper Citation Record · LEDGER

Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.00552.

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

pith.paper-citation-record.v1
2406.00552 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:48:20.468192Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T06:16:28.064256Z

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 f02a6b14-0847-4c9a-939e-0edede69727d · inbound

Geometric deep learning assists protein engineering. Opportunities and Challenges cites this paper.

Geometric deep learning assists protein engineering. Opportunities and Challenges Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:20.468192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:48:20.468192Z digest=sha256:ca1c30f25d9a4a81329ec4c2a651eb215f7b7ab1604013e0669f2e9970b9ae71

Observation a472e81a-27f6-4667-9c1e-a440b5fb5a55 · inbound

Graph Neural Networks to Predict Coercivity of Hard Magnetic Microstructures cites this paper.

Graph Neural Networks to Predict Coercivity of Hard Magnetic Microstructures Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-06T21:41:38.600907Z

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=2026-08-06T21:41:37.019769Z digest=sha256:c46a4c5041d63b44d8e8031127015e48209bd62b512ceb1f12f152cf793bf193