Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.14532.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-25T07:57:39.809343Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T08:00:29.806850Z
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 fb4405ef-c9e3-4f7a-9063-4b3f06a7b10b · inbound
The feasibility of multi-graph alignment: a Bayesian approach cites this paper.
The feasibility of multi-graph alignment: a Bayesian approach Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T02:52:26.983175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ca60765e-6224-4c55-be48-bf46bc538191 · inbound
The feasibility of multi-graph alignment: a Bayesian approach cites this paper.
The feasibility of multi-graph alignment: a Bayesian approach Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T08:00:29.810351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.