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Paper Citation Record · LEDGER

Breaking the Limits of Message Passing Graph Neural Networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2106.04319.

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

pith.paper-citation-record.v1
2106.04319 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:32:59.035034Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:37:19.171943Z

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 24cf284f-f40a-48db-a74e-07db5ff4228b · inbound

Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning cites this paper.

Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning Breaking the Limits of Message Passing Graph Neural Networks

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-11T05:32:59.035034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:32:59.035034Z digest=sha256:a30559581680a1f7dde1812bb395bf357f0aa44470a11ad056e880ff58e37654

Observation 5233e255-f45a-4d05-9195-a6c188cacfc7 · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Breaking the Limits of Message Passing Graph Neural Networks

Reference 109

Resolution
verified exact
arxiv_id, observed 2026-07-02T19:37:19.173360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-27T21:27:50.941166Z digest=sha256:f22cdf8bb47f62d4a012c37ae3b85b0f8408cb6bc97dcf7238c575a4da8c344d