Paper Citation Record · LEDGER

Generalization bounds for graph convolutional neural networks via Rademacher complexity

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2102.10234.

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

pith.paper-citation-record.v1
2102.10234 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:15:29.396923Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:46:55.889503Z

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 dfcb7397-dd14-469e-97e1-ec4b046fd8d9 · inbound

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks cites this paper.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Generalization bounds for graph convolutional neural networks via Rademacher complexity

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T22:15:29.396923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51ee8957-0d99-4202-a201-552964280a87 · inbound

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

Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks Generalization bounds for graph convolutional neural networks via Rademacher complexity

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T08:05:59.200543Z

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.

Observation 4d2c5211-97f9-4838-ad07-9b4cd9d32e9e · inbound

Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective cites this paper.

Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective Generalization bounds for graph convolutional neural networks via Rademacher complexity

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:47:53.572792Z

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.

Observation 9da708f2-5979-4fc7-a8f2-ca337408492d · inbound

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis cites this paper.

PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis Generalization bounds for graph convolutional neural networks via Rademacher complexity

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:46:55.890927Z

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.