Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Simplifying the Theory on Over-Smoothing
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.11876.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:04:08.132753Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T19:01:37.470992Z
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 a555038d-254e-44b2-93d7-fb9eb7f90eee · inbound
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks cites this paper.
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Simplifying the Theory on Over-Smoothing
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T19:01:37.476002Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5eb57617-2398-43f1-be36-2c033bc4bd08 · inbound
What Can We Learn From MIMO Graph Convolutions? cites this paper.
What Can We Learn From MIMO Graph Convolutions? Simplifying the Theory on Over-Smoothing
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:04:08.132753Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1ee31bb3-4dc3-4609-ab79-44f0735c3400 · inbound
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective cites this paper.
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective Simplifying the Theory on Over-Smoothing
Reference 101
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T19:24:57.557714Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.