Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2311.07073.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T16:47:18.679239Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T05:30:23.158908Z
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 ad602e2f-2219-44cf-9261-c25fd8ef3df9 · inbound
Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond cites this paper.
Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T16:47:18.679239Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a20d2ff8-c089-4101-a640-98cd97761036 · inbound
TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows cites this paper.
TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges
Reference 85
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T23:39:46.347371Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ff35ac67-a03e-452b-b9a9-d45e4a765191 · inbound
S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning cites this paper.
S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:30:23.161049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.