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Paper Citation Record · LEDGER

On Self-Distilling Graph Neural Network

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2011.02255.

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

pith.paper-citation-record.v1
2011.02255 v2

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:03:41.066787Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T04:45:54.753685Z

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 cab61e49-936c-4831-bbe0-bec410805bbd · inbound

GNN's Uncertainty Quantification using Self-Distillation cites this paper.

GNN's Uncertainty Quantification using Self-Distillation On Self-Distilling Graph Neural Network

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T23:03:41.066787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:03:41.066787Z digest=sha256:7c3ff39c83cbe44aff1482f0b054c4b75e9590a1e35fa5487e0a5d3567656760

Observation 9dd60499-2062-4f7c-b0ca-7b8c9a589fc4 · inbound

From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning cites this paper.

From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning On Self-Distilling Graph Neural Network

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:45:54.775854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-11T01:08:24.251390Z digest=sha256:b15780575eb3ddda89172578b7a30680a2d299a564ecf0ccbe1b58de1ce6caaa