Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1911.07323.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:35:18.654691Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T20:43:15.329747Z
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 f4db66f4-d9ed-4802-89a2-95671b82c76d · inbound
Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report cites this paper.
Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Reference 59
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-10T21:35:18.654691Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91a10a6b-0091-4b39-8040-0c2c2a6c6640 · 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 Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T20:43:15.331183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.