Paper Citation Record · LEDGER

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams

As of 18 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2508.14218.

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

pith.paper-citation-record.v1
2508.14218 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:48:33.904816Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:47:58.941193Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:47:59.091363Z

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy30
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9944461d-1aa4-4e1d-a8ac-4c7cf3c3ce6f · outbound

This paper cites Graph neural networks: A review of methods and applications,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Graph neural networks: A review of methods and applications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:41.114568Z

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Observation 59139c3b-8ef9-4991-8fdb-77ae2480b5b7 · outbound

This paper cites A theory for multiresolution signal decomposition: the wavelet representation,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams A theory for multiresolution signal decomposition: the wavelet representation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:40.885722Z

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Observation 1c7777dc-fd2f-47ba-8b00-844c1db203d6 · outbound

This paper cites Slic superpixels compared to state-of-the-art superpixel methods,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Slic superpixels compared to state-of-the-art superpixel methods,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:40.731820Z

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Observation 4a46b73d-f5fa-49ef-ae34-a7befeeff5f8 · outbound

This paper cites Superpixels and polygons using simple non- iterative clustering,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Superpixels and polygons using simple non- iterative clustering,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:40.584745Z

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Observation 2c9f6d70-fc16-49ed-89c0-9c4bba0b6cff · outbound

This paper cites Turbopixels: Fast superpixels using geometric flows,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Turbopixels: Fast superpixels using geometric flows,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:40.453429Z

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Observation 04e675f5-907c-4eb9-8c19-daa2f8c15818 · outbound

This paper cites Linear spectral clustering superpixel,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Linear spectral clustering superpixel,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:40.328752Z

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Observation 4c2de011-2897-483f-8cd1-f7b52bf11380 · outbound

This paper cites An extensive survey on superpixel segmenta- tion: A research perspective,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams An extensive survey on superpixel segmenta- tion: A research perspective,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:40.216503Z

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Observation 280adcf4-4a38-4127-a71c-30b5bd769010 · outbound

This paper cites V oronoi diagrams — a survey of a fundamental geometric data structure,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams V oronoi diagrams — a survey of a fundamental geometric data structure,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:40.072956Z

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Observation ce3d9473-d485-4fee-9c04-949b54699f9c · outbound

This paper cites Triangulations from topologically correct digital voronoi diagrams,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Triangulations from topologically correct digital voronoi diagrams,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:39.912756Z

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Observation 9868099d-317a-4cb3-b2fd-c8c8448250bb · outbound

This paper cites Approximation algorithms for the vertex k-center problem: Survey and experimental evaluation,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Approximation algorithms for the vertex k-center problem: Survey and experimental evaluation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:39.704241Z

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Observation 3042c3db-83ea-4a13-a0e0-773653018e7c · outbound

This paper cites Least squares quantization in pcm,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Least squares quantization in pcm,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:39.491206Z

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Observation 4bb4d5b4-7232-4456-99c2-2789f408d70b · outbound

This paper cites Some methods for classification and analysis of multivariate observations,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Some methods for classification and analysis of multivariate observations,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:39.290976Z

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Observation fb1b0a3b-a723-4dd5-9eda-612c9065e2ff · outbound

This paper cites A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:39.105502Z

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Observation 7637e577-49e7-42df-87a4-8c39642f1ff2 · outbound

This paper cites A survey of fuzzy clustering,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams A survey of fuzzy clustering,

Reference 14

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Observation 3ed3bc48-289d-4a5e-8e0a-d0a8fc8812b6 · outbound

This paper cites The graph neural network model,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams The graph neural network model,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:38.706473Z

Source-reported events for the cited work

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Observation f2321d3a-67ee-4489-9d54-1cb8670d890c · outbound

This paper cites Neural message passing for quantum chemistry,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Neural message passing for quantum chemistry,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:38.501826Z

Source-reported events for the cited work

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Observation 069ead52-84c2-4a14-b22c-057998c367a1 · outbound

This paper cites Graph attention networks,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Graph attention networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:38.318465Z

Source-reported events for the cited work

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Observation 72c0e25a-9964-4a34-86e5-36ffc924ffe9 · outbound

This paper cites Algorithms for the reduction of the number of points required to represent a digitalized line or its caricature,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Algorithms for the reduction of the number of points required to represent a digitalized line or its caricature,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:38.202812Z

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Observation 6d32a093-4b2b-4e90-8574-9fb70774405c · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Semi-Supervised Classification with Graph Convolutional Networks

Reference 19

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Observation fca79ab8-61c8-4251-89f9-8977aba29c6d · outbound

This paper cites de Berg and et al., Computational Geometry: Algorithms and Applications, 3rd ed.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams de Berg and et al., Computational Geometry: Algorithms and Applications, 3rd ed

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:37.957040Z

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Observation 06e777f7-7652-48d1-9c14-f2d2206649cf · outbound

This paper cites Fast graph representation learning with PyTorch Geometric,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Fast graph representation learning with PyTorch Geometric,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:37.795513Z

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Observation 26f8c333-ced7-425c-bbb7-ba2de1d82425 · outbound

This paper cites Image classification using graph neural network and multiscale wavelet superpixels,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Image classification using graph neural network and multiscale wavelet superpixels,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:37.581440Z

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Observation c064f3bc-ee8f-4172-9669-09f2832dd23f · outbound

This paper cites Superpixel image classification with graph attention networks,.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Superpixel image classification with graph attention networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:37.447084Z

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Observation 3679e0f3-f014-4b8d-9499-a94ed5bf5c01 · outbound

This paper cites Here’s the time complexity analysis while explicitly incorporating the degree matrix.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Here’s the time complexity analysis while explicitly incorporating the degree matrix

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:37.248256Z

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Observation b0b958b0-477c-4341-8306-a03e3ad9709b · outbound

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Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 25

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Observation 19372c08-32a0-4aad-8006-09a9336a9cbd · outbound

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Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 26

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Observation 289c9a94-62cf-40cf-916c-48dbfc08ebeb · outbound

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Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 27

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Observation 20e6c1ed-a6db-4a9f-9a69-66069164aed1 · outbound

This paper cites Multiplying a diagonal matrix with another matrix is relatively simple: each row of the feature matrix H (l) is scaled by the corresponding diagonal element in ˆD− 1 2.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Multiplying a diagonal matrix with another matrix is relatively simple: each row of the feature matrix H (l) is scaled by the corresponding diagonal element in ˆD− 1 2

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:36.503237Z

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Observation c9c0fb01-f176-49b9-99d7-27db99feb141 · outbound

This paper cites The matrix multiplication ˜AH (l) will take O(M 2F ) in a dense graph.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams The matrix multiplication ˜AH (l) will take O(M 2F ) in a dense graph

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:36.283054Z

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Observation d1b2073b-dfd9-48d2-9f1f-09126106d440 · outbound

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Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 30

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Observation 8a4c2801-4010-4a39-8638-ce7777e9cf08 · outbound

This paper cites This is a standard matrix multiplication where: - H (l) is of size [M ×F ].

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams This is a standard matrix multiplication where: - H (l) is of size [M ×F ]

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:35.877377Z

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Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 32

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Observation 70ec9d3e-1e3e-415a-87ef-67ad11735826 · outbound

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Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 33

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Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 34

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Observation 6102932c-ec13-49db-8baf-f22877d5dae0 · outbound

This paper cites • Adjacency Matrix Multiplication ˜AH (l): For a sparse graph like Delaunay tessellation, this requires O(M F) multiplications.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams • Adjacency Matrix Multiplication ˜AH (l): For a sparse graph like Delaunay tessellation, this requires O(M F) multiplications

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:35.089041Z

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Observation 2c7363c6-e7f6-4363-8a10-17cb68a1606d · outbound

This paper cites • Weight Matrix Multiplication H (l) D.T W (l): This requires O(M F F′) multiplications.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams • Weight Matrix Multiplication H (l) D.T W (l): This requires O(M F F′) multiplications

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:34.910794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 130078fd-2594-4215-838f-98be4779779a · outbound

This paper cites In this case, the percentage reduction is: Percentage Reduction = 2 3 + 1× 100 = 2 4 × 100 = 50% This means removing the degree matrix leads to a 50% reduction in multiplications.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams In this case, the percentage reduction is: Percentage Reduction = 2 3 + 1× 100 = 2 4 × 100 = 50% This means removing the degree matrix leads to a 50% reduction in multiplications

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:34.686976Z

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Observation 42c25f92-4664-487a-8d75-4296be2a50bf · outbound

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Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 38

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Observation 4ad71ded-1240-42f4-8a80-602aeb70d3c1 · outbound

This paper cites an unresolved cited work.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-05T18:48:34.341313Z

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Observation 789a1c1b-9bac-403c-a85e-bea837d12db2 · outbound

This paper cites Conclusion Therefore, finding the optimal number of regions in image segmentation is NP-Hard because it can be reduced to the K- Center Problem.

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams Conclusion Therefore, finding the optimal number of regions in image segmentation is NP-Hard because it can be reduced to the K- Center Problem

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:48:34.132613Z

Source-reported events for the cited work

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Pith citing papers

Observation dc9d1b47-2ba5-4123-a029-46c8ec679766 · inbound

Hot Rocks Survey IV: Emission from LTT 3780 b is consistent with a bare rock cites this paper.

Hot Rocks Survey IV: Emission from LTT 3780 b is consistent with a bare rock Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:47:59.168307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.