GEFRFE extends generalized frequency filtering embedding into graph fractional Fourier domains via fractional Laplacian eigenvectors, nonlinear composition, and adaptive fractional order selection to improve graph classification on benchmarks.
The emerging field of signal processing on graphs: Ex- tending high-dimensional data analysis to networks and other irregular domains,
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Introduces quantitative error feedback from digital filter techniques to exactly compensate quantization noise in graph filtering, with closed-form optimal coefficients for deterministic, random-graph, and asynchronous scenarios.
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Graph Embedding in the Graph Fractional Fourier Transform Domain
GEFRFE extends generalized frequency filtering embedding into graph fractional Fourier domains via fractional Laplacian eigenvectors, nonlinear composition, and adaptive fractional order selection to improve graph classification on benchmarks.
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Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs
Introduces quantitative error feedback from digital filter techniques to exactly compensate quantization noise in graph filtering, with closed-form optimal coefficients for deterministic, random-graph, and asynchronous scenarios.