Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2302.13522.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:07:36.736118Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T09:45:40.703484Z
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 1ce1bb25-63b9-4e82-a5b2-18738c6a3846 · inbound
Training a Label-Noise-Resistant GNN with Reduced Complexity cites this paper.
Training a Label-Noise-Resistant GNN with Reduced Complexity IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:07:19.089711Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation acb622a0-1b12-4d02-9cbb-6deb184c52cf · inbound
Fused3S: Fast Sparse Attention on Tensor Cores cites this paper.
Fused3S: Fast Sparse Attention on Tensor Cores IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:07:36.736118Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f1a3855-9cc0-4fdb-b8fa-7347d87f4afb · inbound
FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning cites this paper.
FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Reference 68
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T09:45:40.704912Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.