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Paper Citation Record · LEDGER

Node-Level Differentially Private Graph Neural Networks

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.15521.

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

pith.paper-citation-record.v1
2111.15521 v3

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 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T23:04:43.841278Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T23:14:02.127042Z

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 3685562f-0e57-4fc1-b82a-ccf2ec8dfdfe · inbound

Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability cites this paper.

Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability Node-Level Differentially Private Graph Neural Networks

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:21:06.975163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-09T17:27:56.132442Z digest=sha256:bca9d6488f2ec1e3a9c546ea3d80fc2a893f15367211f0db1df970d6824cddc6

Observation 9953e88e-856e-4a11-bcfa-77f3673a6902 · inbound

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks cites this paper.

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks Node-Level Differentially Private Graph Neural Networks

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-06-29T23:14:02.128574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-29T23:04:43.841278Z digest=sha256:fa77b397890af5544ea01c6639cd42fe273203b8592c38112ff4c58fcabe7cad