Paper Citation Record · LEDGER

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2203.12852.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2203.12852 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:24.676792Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T09:38:10.699865Z

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 7b129644-a98b-4643-b52d-6ab01dcb7b70 · inbound

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection cites this paper.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:24.676792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 395ef2bb-3461-4769-8ebd-b7c0c590546d · inbound

Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters cites this paper.

Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T20:44:21.575911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af2c6be3-04c3-400d-b8cf-a3f50b038d1d · inbound

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties cites this paper.

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:03.909543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c13330b-e1eb-4ff1-b120-db32d2f14c05 · inbound

NuGraph2 with Context-Aware Inputs: Physics-Inspired Improvements in Semantic Segmentation cites this paper.

NuGraph2 with Context-Aware Inputs: Physics-Inspired Improvements in Semantic Segmentation Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T17:02:44.303808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1bd3485f-3742-42b5-82d2-1755ccb00d3a · inbound

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging cites this paper.

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T01:08:47.544957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1beff4cb-ca68-4002-b777-8e492f7a6103 · inbound

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging cites this paper.

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T18:00:50.915396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77a69bf7-53fb-419a-9563-2bae865d461b · inbound

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane cites this paper.

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:01:14.454815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b39fcbbc-6df1-4034-af6c-bab04624b6ae · inbound

Dissecting Jet-Tagger Through Mechanistic Interpretability cites this paper.

Dissecting Jet-Tagger Through Mechanistic Interpretability Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:27.962854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6ebcaad4-4ead-4677-9684-5e6611c5666a · inbound

Probing SMEFT Operators through $t\bar{t}t\bar{t}$ Production with Hyper-Graph Neural Networks at the LHC cites this paper.

Probing SMEFT Operators through $t\bar{t}t\bar{t}$ Production with Hyper-Graph Neural Networks at the LHC Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-20T09:38:10.701901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.