Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2104.07145.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:03:56.883134Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T23:57:29.094466Z
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 74e44756-fb79-4144-8379-0c89af83aa34 · inbound
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks cites this paper.
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:03:56.883134Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation acc92b24-b518-4bf5-bf26-93889bbd5c7d · inbound
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling cites this paper.
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:06:37.382067Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93066199-837b-4798-9e71-8c217218c090 · inbound
Secure Federated Graph-Filtering for Recommender Systems cites this paper.
Secure Federated Graph-Filtering for Recommender Systems FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T05:55:14.309687Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d9f11b9-a01e-4ffb-8930-389c6624fd6c · inbound
Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data cites this paper.
Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:45:31.938957Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c5711e2e-1a21-4f6d-b7fe-f44b5dbeb6cb · inbound
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability cites this paper.
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T12:24:10.724961Z
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 e20b5f9e-c9c3-420d-95f3-706d9a17824f · inbound
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment cites this paper.
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T02:51:18.048517Z
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 a926077c-113b-45bf-a4ba-6748880e6b0e · inbound
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning cites this paper.
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T07:42:30.451841Z
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 05dae213-7264-47c0-ad28-de57e846191e · inbound
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks cites this paper.
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T23:14:02.137342Z
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 61b2d797-e349-4124-adb7-08e86f27423c · inbound
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning cites this paper.
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Reference 10
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T23:57:29.095904Z
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.