Paper Citation Record · LEDGER

Survey on Generalization Theory for Graph Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2503.15650.

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

pith.paper-citation-record.v1
2503.15650 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:31:43.556743Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 415aa303-8e2f-4105-b838-0182bc4b6ed8 · inbound

From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs cites this paper.

From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs Survey on Generalization Theory for Graph Neural Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:31:43.556743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c655afd-d3f4-4782-b8c9-cfd65e2f99cf · inbound

Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning cites this paper.

Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning Survey on Generalization Theory for Graph Neural Networks

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:21:32.766659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 866b58f6-20ed-4bd4-8441-6e57b462bcd6 · inbound

On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry cites this paper.

On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry Survey on Generalization Theory for Graph Neural Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T10:24:25.197927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e04b62e-8771-4145-a840-c5a224772be0 · inbound

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation cites this paper.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Survey on Generalization Theory for Graph Neural Networks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.509061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 124c41d6-b150-48e1-97e1-f54f2ef39647 · inbound

Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks cites this paper.

Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks Survey on Generalization Theory for Graph Neural Networks

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:05:59.186796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6907c09f-98d5-4546-950d-36ade0013307 · inbound

Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States cites this paper.

Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States Survey on Generalization Theory for Graph Neural Networks

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-08T23:49:28.059704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ba39d700-39db-46d9-a1a5-43bd7c6fe2ff · inbound

Bridging Input Feature Spaces Towards Graph Foundation Models cites this paper.

Bridging Input Feature Spaces Towards Graph Foundation Models Survey on Generalization Theory for Graph Neural Networks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:41:08.749490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4cadb04f-7f42-4344-afc2-f44ab57ee1e7 · inbound

When and How to Canonize: A Generalization Perspective cites this paper.

When and How to Canonize: A Generalization Perspective Survey on Generalization Theory for Graph Neural Networks

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T00:57:00.746483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.