Methods › Graphs › Graph Models › CayleyNet

CayleyNet

1 paper tagged archive 2025-07-28

Introduced by Ron Levie et al. in CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

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

The core ingredient of CayleyNet is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. The model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators.

Description adapted from: CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

PaperSource

Papers archive 2025-07-28

1 shown of 1, 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

7 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
Community Detection1
Deep Learning1
General Classification1
Image Classification1
Matrix Completion1
Node Classification1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with CayleyNet: 2017 to 2017, peak 1 1 0 2017: 1 paper 2017
Papers per year the archive tags with this method, by the paper's archive date (1 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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