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Paper Citation Record · LEDGER

Degree-Quant: Quantization-Aware Training for Graph Neural Networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2008.05000.

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

pith.paper-citation-record.v1
2008.05000 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:25:18.485161Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T23:15:25.078519Z

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 7945c91e-e94d-4453-8a1f-45bedaf84fcf · inbound

FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs cites this paper.

FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs Degree-Quant: Quantization-Aware Training for Graph Neural Networks

Reference 32

Resolution
verified exact
local_arxiv, observed 2026-08-10T23:15:25.085369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-10T23:15:24.908925Z digest=sha256:4d35f1eb417efd51b95326d1297e79246c902f74c4a8929c1db2c93affb02a58

Observation 97818df8-e785-4e30-bba7-3bd2d20cf0a5 · inbound

Inference-friendly Graph Compression for Graph Neural Networks cites this paper.

Inference-friendly Graph Compression for Graph Neural Networks Degree-Quant: Quantization-Aware Training for Graph Neural Networks

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-16T12:25:18.485161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:25:18.485161Z digest=sha256:1f2b6eb8f37dc673b82996f99508368bb3a3ee73a2b1c230451c41000e57277b

Observation 6f7f5576-f1fe-46f1-9b16-6f370c2c754c · inbound

Diffusion Model Quantization: A Review cites this paper.

Diffusion Model Quantization: A Review Degree-Quant: Quantization-Aware Training for Graph Neural Networks

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-15T23:13:35.588041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:13:35.588041Z digest=sha256:4e4a2336ad84c93c72e5a32543d0b770d605bfe562590d7b02d603654207f899