Paper Citation Record · LEDGER

Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification

As of 21 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.

pith.paper-citation-record.v1
1905.04579 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:25:02.919582Z

measured 1 of 1 external citation measurements

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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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.