Rotation-Parameterized Graph Fractional Fourier Transform: Definition, Properties, and Optimal Filtering
arXiv:2511.16111v2 Announce Type: replace-cross Abstract: Graph spectral representations are fundamental in graph signal processing, providing a rigorous frameworkforanalyzing graph-structured data. The graph fractional Fourier transform (GFRFT) extends the graph Fourier transform (GFT) through a fractional-order parameter, enabling flexible spectral analysis with mathematical consistency. The angular graph Fourier transform (AGFT) further introduces angular control by rotating GFT eigenvectors; however, existing constructions may fail to reduce exactly to the GFT at zero angle, weakening theo
This is a new academic paper published on arXiv, representing incremental progress in theoretical computer science, a continuous process in academic research.
A strategic reader focused on practical applications or market-moving developments would not find this theoretical paper directly relevant.
No immediate or foreseeable changes in the market, technology stack, or geopolitical landscape result from this purely theoretical definition.
Further theoretical understanding of graph signal processing techniques is slightly advanced.
This foundational work may someday contribute to more robust algorithms, but this is distant and uncertain.
Improved algorithms might eventually have minor impacts on AI or data analysis tools in highly specialized settings.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG