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Paper Citation Record · LEDGER

From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.10121.

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

pith.paper-citation-record.v1
2310.10121 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T23:39:46.189334Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T23:39:47.183817Z

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 9dfdd0f0-8b49-4cec-8388-19c77a11e714 · inbound

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows cites this paper.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

Reference 47

Resolution
verified exact
local_arxiv, observed 2026-08-05T23:39:47.262395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T23:39:46.189334Z digest=sha256:b0a34a85bb07601f3a000094b83c72468afe9020d14b6b32fe111eabd45b7a9d

Observation 6684dd35-a48d-4afa-8795-7f326b31c109 · inbound

Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement cites this paper.

Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-01T06:31:21.387518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T06:31:21.387518Z digest=sha256:0886c6bf995165531ff9d16a0d5e45389f7ebf791dccb88d8b3a0a89334c1a86