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Paper Citation Record · LEDGER

Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2212.09034.

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

pith.paper-citation-record.v1
2212.09034 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 2026-08-04T16:34:04.147575Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 99eddb37-1b05-4b26-b144-1fefbfc0ddc6 · inbound

Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis cites this paper.

Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-04T16:34:04.147575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T16:34:04.147575Z digest=sha256:c3730aa0b46f58c35798fcd84a1e8dd25fff4c04cee2ad148f9306c6217a84bb

Observation be2db689-c64a-49eb-9244-5dca0bf0ebd5 · inbound

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure cites this paper.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-02T14:52:38.071261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:52:38.071261Z digest=sha256:9e03e061244ecd638c4f67f3eec35f556e2791c7406476de287417ef13d0d4f6