Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2203.10983.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T10:51:16.274084Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T20:43:15.376334Z
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 9bd2c9f2-1963-414b-b1dc-b2ed57b5faba · inbound
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks cites this paper.
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T10:51:16.274084Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81158864-e5a6-41f4-93cd-4dd0bf83158f · inbound
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training cites this paper.
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T20:43:15.377735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c6ccbf1e-7f54-43d0-a1b2-a809e067412f · inbound
SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction cites this paper.
SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T04:30:57.313788Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.