Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.01535.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:26:20.763495Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T06:36:43.975496Z
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 5ce0b845-c42c-4ad7-b3ee-2e24bccd5c0e · inbound
Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization cites this paper.
Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:26:20.763495Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 01c60394-7452-4df9-a7c3-368158e7b08f · inbound
GraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories cites this paper.
GraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T14:01:27.146295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e99a7e87-e6cb-461a-979f-43799c0af7c2 · inbound
FLAGG: Flexible Autoregressive Graph Generation cites this paper.
FLAGG: Flexible Autoregressive Graph Generation Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T06:36:43.977024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.