Spectral Graph Uncertainty Principles via the Graph Fractional Fourier Transform

Abstract

This paper develops a graph fractional uncertainty principle in the graph fractional Fourier transform (GFRFT) domain. We introduce localization operators in the vertex domain and the graph fractional spectral domain, and build an operator framework to characterize the joint localization of graph signals. A sandwiched joint localization operator is first constructed, whose largest eigenvalue quantifies the attainable simultaneous concentration in the two domains. Then, rotated localization operators and the numerical range are used to provide a geometric description of the admissible uncertainty region, together with a polygonal approximation method for its computation. Numerical examples show that the fractional order reshapes the vertex-graph fractional spectral localization trade-off, and enlarge or shrink the uncertainty region relative to the graph Fourier transform based case. These results generalize classical graph uncertainty principles to the GFRFT domain and provide a flexible tool for graph fractional signal analysis and graph-adapted localized representations.