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Paper Citation Record · LEDGER

Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.11243.

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

pith.paper-citation-record.v1
2408.11243 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:36:36.411344Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T04:52:17.365815Z

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 1d34e6dc-5122-484a-a994-3764da8c06eb · inbound

GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning cites this paper.

GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-05T14:36:36.411344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:36:36.411344Z digest=sha256:344ef128cb95046252a55e0bfeec8f22ab70d8ee59d1aff5d4968bacc3eaad2c

Observation e4d08721-d6cd-4935-b45a-3749a009f453 · inbound

SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory cites this paper.

SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?

Reference 83

Resolution
verified exact
arxiv_id, observed 2026-05-13T04:52:17.367273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-05-13T04:45:34.957298Z digest=sha256:a29afda50a52a50c6e5caa40969f6ced222df2075b436d4d39e69eb38224cc8b