Paper Citation Record · LEDGER

FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2102.04925.

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

pith.paper-citation-record.v1
2102.04925 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:55:27.296486Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:49:57.856726Z

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 172c31b4-e7c5-47ef-a2a9-1c8e7afb358c · inbound

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks cites this paper.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:27.296486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be41806b-6ecc-41a7-87d6-ed9199e187c2 · inbound

A Comprehensive Data-centric Overview of Federated Graph Learning cites this paper.

A Comprehensive Data-centric Overview of Federated Graph Learning FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.925144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73861e62-9d5e-46ab-b59c-10b2953953c9 · inbound

Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation cites this paper.

Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T20:33:52.759304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30b1d986-347c-4718-ba0b-625c81bd0a65 · inbound

Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration cites this paper.

Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:56:07.317338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3eaaf9cf-0c6a-4512-a64b-5d77fbfc5707 · inbound

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

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:02.097409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca2199a0-9485-4c2e-8d2a-8d9a31bf7354 · inbound

Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach cites this paper.

Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:49:57.858378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.