Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
What graph neural networks cannot learn: depth vs width
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1907.03199.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T20:29:46.597246Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T07:04:41.445060Z
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 05dfd8ad-a7a9-42a2-ab95-b5fb5a10fe0a · inbound
Computing and Learning on Combinatorial Data cites this paper.
Computing and Learning on Combinatorial Data What graph neural networks cannot learn: depth vs width
Reference 214
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T20:29:46.597246Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f7f895e-d2f0-4647-a086-30823c06b47f · inbound
Future Link Prediction Without Memory or Aggregation cites this paper.
Future Link Prediction Without Memory or Aggregation What graph neural networks cannot learn: depth vs width
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:18:38.716308Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8ea97d15-db01-4e13-a495-b7f546918062 · inbound
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation cites this paper.
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation What graph neural networks cannot learn: depth vs width
Reference 2007
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:50:08.542762Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ef2177a8-13c9-4052-b707-79651f04125b · inbound
Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers cites this paper.
Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers What graph neural networks cannot learn: depth vs width
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T07:04:41.447278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 80d52464-2eca-4060-a2c3-b1cfda5af7b8 · inbound
Universality and Approximation Rates of Graph Neural Networks with Random Features cites this paper.
Universality and Approximation Rates of Graph Neural Networks with Random Features What graph neural networks cannot learn: depth vs width
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-07-30T23:40:06.731824Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.