Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1905.04579.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T11:25:02.919582Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
52
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation c917c878-bf36-4e8f-bad3-6308f7109f84 · inbound
Graph Structure Refinement with Energy-based Contrastive Learning cites this paper.
Graph Structure Refinement with Energy-based Contrastive Learning Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:25:02.919582Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 375bf40f-cd3f-4572-bc05-e6705cd182c6 · inbound
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets cites this paper.
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T12:29:27.617681Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 86cdae0e-c0f7-4382-a181-5fda56f6e4c1 · inbound
Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems cites this paper.
Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T02:51:59.753000Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation abb1c054-2a3b-4b46-ad56-08e7f1ba7dfd · inbound
Learning over Positive and Negative Edges with Contrastive Message Passing cites this paper.
Learning over Positive and Negative Edges with Contrastive Message Passing Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T12:33:16.854754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation dd7d1ddc-9ebb-422d-9718-fe093132b272 · inbound
Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nystr\"om Method cites this paper.
Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nystr\"om Method Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T19:29:58.375042Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.