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ChebNet

6 papers tagged archive 2025-07-28

Introduced by Michaël Defferrard et al. in Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ChebNet involves a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.

Description from: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

PaperSource

Papers archive 2025-07-28

6 shown of 6, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Node Classification4
GPR2
Graph Neural Network2
Cancer Classification1
Graph Attention1
Graph Learning1
Graph Reconstruction1
Graph Representation Learning1
Node Classification on Non-Homophilic (Heterophilic) Graphs1
Representation Learning1
Skeleton Based Action Recognition1

Usage over time archive 2025-07-28

Papers per year tagged with ChebNet: 2016 to 2024, peak 2 2 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 2 papers 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (6 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Graph Models

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