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Paper Citation Record · LEDGER

On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.01009.

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

pith.paper-citation-record.v1
2405.01009 v2

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:57:29.146615Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:57:37.106491Z

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 86dde8b2-1ef1-45b0-bd6d-d02a8c0fcc90 · inbound

GRAMA: Adaptive Graph Autoregressive Moving Average Models cites this paper.

GRAMA: Adaptive Graph Autoregressive Moving Average Models On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-10T16:57:29.146615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T16:57:29.146615Z digest=sha256:3ef55bcc9356f9803d431e8b4938cb9973670a99098467e8e6a5d5590dd43b6b

Observation 3e80009f-dc6a-4ee4-93e1-9be07c5a40fb · inbound

Improving the Effective Receptive Field of Message-Passing Neural Networks cites this paper.

Improving the Effective Receptive Field of Message-Passing Neural Networks On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:57:37.139727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T12:57:33.592594Z digest=sha256:3ced57f394da4cebf93e5600bc247a41856b2abe4718e0434c9c99794e54e754