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Paper Citation Record · LEDGER

Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.11129.

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

pith.paper-citation-record.v1
2409.11129 v1

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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:36:02.126115Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:16:01.085092Z

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 64f373bf-9054-4448-9847-57a60e61e4df · inbound

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks cites this paper.

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T04:36:02.126115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:36:02.126115Z digest=sha256:4d084532d93b50212110cb8db35ab6ec9566bf7f866afaab92bfe1cfcc22adc3

Observation e248d69f-fde4-47a7-9966-0313555ed6ec · inbound

On Efficient Scaling of GNNs via IO-Aware Layers Implementations cites this paper.

On Efficient Scaling of GNNs via IO-Aware Layers Implementations Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:16:01.086798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T22:52:59.081816Z digest=sha256:d5b685f3e86ddaa79fe2cc8ca4f88b08fa075a4cb3846e2122304a490e1843ec