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Paper Citation Record · LEDGER

Understanding convolution on graphs via energies

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2206.10991.

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

pith.paper-citation-record.v1
2206.10991 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:51:26.464405Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T17:14:56.823672Z

Reference resolution

0 of 0 outbound references displayed

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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 d48ef345-abcf-4700-8a07-1705e5eb7da3 · inbound

Personalized Layer Selection for Graph Neural Networks cites this paper.

Personalized Layer Selection for Graph Neural Networks Understanding convolution on graphs via energies

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.464405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.464405Z digest=sha256:1dae2a019c8ee4da5290f40a17fd19c7df8d0e2726502881d98bab2cc7ddcf70

Observation 6cf2c913-eb5d-42d7-8e7e-fc7513a46210 · inbound

Resolving Oversmoothing with Opinion Dissensus cites this paper.

Resolving Oversmoothing with Opinion Dissensus Understanding convolution on graphs via energies

Reference 44

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unresolved
no resolver link, observed 2026-08-09T21:29:27.915677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.915677Z digest=sha256:a0330bdeb098a06f85cb5ba1c7937b97c2136b670ca39b37c26fbfbc47556d8b

Observation eb297e29-097c-4ec7-8e30-ffa7ffbe3757 · inbound

What makes a good feedforward computational graph? cites this paper.

What makes a good feedforward computational graph? Understanding convolution on graphs via energies

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-08T14:33:15.934076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T14:33:15.934076Z digest=sha256:5c16e3460a1f3ff14d7406c4f60bd1f1ae490a84fdbbc26991636f85789fc7b0

Observation 1c6d6caf-108c-4418-b375-cc083cc56ca5 · inbound

Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion cites this paper.

Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion Understanding convolution on graphs via energies

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T11:05:12.857963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:05:12.857963Z digest=sha256:5e8431ad08981e3b7f624762e8efef6a32ccc56dd1634449185918d2ede99fd8

Observation 30ef06ba-f79f-4ca9-9d40-6a58fd146348 · inbound

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

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Understanding convolution on graphs via energies

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T23:39:46.095588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d5d9e79d-8e9e-40cc-b936-4750108cc355 · inbound

Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors cites this paper.

Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors Understanding convolution on graphs via energies

Reference 112

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verified exact
arxiv_id, observed 2026-05-11T12:21:04.947198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 6fa761f6-9d63-4195-887a-5c71908ca568 · inbound

Graph Navier Stokes Networks cites this paper.

Graph Navier Stokes Networks Understanding convolution on graphs via energies

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:13:58.221137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5b91947c-be13-4bdc-8841-d87fc00a3236 · inbound

Graph Navier Stokes Networks cites this paper.

Graph Navier Stokes Networks Understanding convolution on graphs via energies

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-06-30T17:14:56.825088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T17:11:15.801586Z digest=sha256:565592ebcaee6916400ed6d497562c5716652d3f0a1b670ba16be11d4f777829

Observation 3b2993af-ed48-4524-90b9-1cc8a5af5447 · inbound

Learning Dynamic Stability Landscapes in Synchronization Networks cites this paper.

Learning Dynamic Stability Landscapes in Synchronization Networks Understanding convolution on graphs via energies

Reference 160

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:05:22.704024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-25T05:04:16.957305Z digest=sha256:9ff1aa57b4a202bb1190c8c020e008647a0a9459b5a6d3c16b45a8a621cf95bc

Observation 31d2cbe8-35b5-4ec7-a8cc-f93ccda6903e · inbound

Benchmarking Sheaf Neural Networks for Inductive Tasks cites this paper.

Benchmarking Sheaf Neural Networks for Inductive Tasks Understanding convolution on graphs via energies

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-04T04:52:31.771665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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