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Paper Citation Record · LEDGER

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks

As of 7 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2510.01022.

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

pith.paper-citation-record.v1
2510.01022 v3

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:23:59.200648Z

measured 17 of 17 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

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  • verified fuzzy0
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2cebc178-2e10-4664-8c59-bfdc53a067df · outbound

This paper cites Geometric GNN Dojo.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Geometric GNN Dojo

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7ef6c947-c99f-4421-ac28-7dd305036ba8 · outbound

This paper cites an unresolved cited work.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-04T13:23:59.048988Z digest=sha256:4cc727c7dda01c25103f80c6ffc86a062e7d47bf36243c81cc5fc7a4ca45441e

Observation 52eacf27-ef13-461e-8161-1e948826eb9d · outbound

This paper cites Manifold filter-combine networks.Sampling Theory, Signal Processing, and Data Analysis, 23(2):17, 2025a.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Manifold filter-combine networks.Sampling Theory, Signal Processing, and Data Analysis, 23(2):17, 2025a

Reference 8

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Observation 54c8cfbb-cca4-4b45-a574-43e0a7cb7bf8 · outbound

This paper cites Revisiting Graph Neural Networks: All We Have is Low-Pass Filters.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Reference 9

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Observation f801844f-834b-4ab3-b84b-5a590bb2f94d · outbound

This paper cites URL https://doi.org/10.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks URL https://doi.org/10

Reference 10

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verified exact
doi, observed 2026-08-04T13:29:27.154744Z

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=2026-08-04T13:23:58.633646Z digest=sha256:03022419bc24a28d49e1ed600f3b2bb30afa5f8d6a06d57d729839764d1effb6

Observation 1ea5768a-e6ea-4ec6-a2d2-c28a77c07fc9 · outbound

This paper cites Convolutional filtering on sampled manifolds.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Convolutional filtering on sampled manifolds

Reference 12

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Unavailable: canonical work link unavailable.

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Observation bf30767b-909f-471c-9bd2-79d75a70b2e1 · outbound

This paper cites Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks

Reference 13

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Observation d6dcaec5-8dda-4d39-9c04-766f46a57789 · outbound

This paper cites [2024].) The key to the proof of Proposition B.2 will be to combine this result with the inequality (6), stated below, that relates this weighted norm to the unweightedℓ 2 norm.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks [2024].) The key to the proof of Proposition B.2 will be to combine this result with the inequality (6), stated below, that relates this weighted norm to the unweightedℓ 2 norm

Reference 14

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source=pdf_text observed=2026-08-04T13:23:58.993671Z digest=sha256:ef995c4497ba326a77a1278171b1f59fc8a008cceff30b38d6b96fe7f1f5dc01

Observation 7ab96938-cf68-4793-9730-e92acda95ad7 · outbound

This paper cites an unresolved cited work.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Unresolved cited work

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T13:23:59.200648Z digest=sha256:81a6727a5cb42af42c82a8e1e90c1757a0c9b1dc97297b19b5b4662961e01ea8

Observation 81f51b1a-6142-4a6e-a824-00862542dba2 · outbound

This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 2012

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Observation 34d4de88-d8af-405d-aba9-84469e6309f6 · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 2017

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Observation f1465311-e0ac-4374-95c6-5eb48a909837 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Fast Graph Representation Learning with PyTorch Geometric

Reference 2018

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Unavailable: canonical work link unavailable.

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Observation 6796aa25-55cb-437b-8a6a-ebe072715e15 · outbound

This paper cites Geometrically Equivariant Graph Neural Networks: A Survey.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks Geometrically Equivariant Graph Neural Networks: A Survey

Reference 2019

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Observation f48fd99f-9d3a-418b-8774-502912900caa · outbound

This paper cites The manifold scattering transform for high-dimensional point cloud data.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks The manifold scattering transform for high-dimensional point cloud data

Reference 2021

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Observation 806bdbec-2062-44ae-a186-84fdb73baa43 · outbound

This paper cites DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveforms.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveforms

Reference 2022

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Observation 55e04679-d37e-48de-9cf3-3ce51b8a0c53 · outbound

This paper cites A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

Reference 2023

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Observation ee841244-d02d-43bb-93d5-29d6afcf9628 · outbound

This paper cites doi: https://doi.org/10.

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks doi: https://doi.org/10

Reference 2024

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Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.