Paper Citation Record · LEDGER

Node-Level Differentially Private Graph Neural Networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 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 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:26:51.523586Z

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 6c3ac16f-fc7e-49e1-8fe5-29b0eedd26cc · inbound

PrivDPR: Synthetic Graph Publishing with Deep PageRank under Differential Privacy cites this paper.

PrivDPR: Synthetic Graph Publishing with Deep PageRank under Differential Privacy Node-Level Differentially Private Graph Neural Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:51.523586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f581742c-dd49-4000-ab66-b7473a9e0cab · inbound

Structure-Preference Enabled Graph Embedding Generation under Differential Privacy cites this paper.

Structure-Preference Enabled Graph Embedding Generation under Differential Privacy Node-Level Differentially Private Graph Neural Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:46.981238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.