2023/06/27 by Elizaveta Rebrova, Rebrova, Elizaveta, Palina Salanevich +1
Biochemistry, Genetics and Molecular Biology · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Fractal and DNA sequence analysis #Gene Regulatory Network Analysis #Information Theory (cs.IT) #Mathematical Analysis and Transform Methods #Optimization and Control (math.OC) #Probability (math.PR)
paper · pdf · doi:10.48550/arxiv.2306.15810
openalex publication_date 2023/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Uncertainty principles present an important theoretical tool in signal processing, as they provide limits on the time-frequency concentration of a signal. In many real-world applications the signal domain has a complicated irregular structure that can be described by a graph. In this paper, we focus on the global uncertainty principle on graphs and propose new connections between the uncertainty bound for graph signals and graph eigenvectors delocalization. We also derive uncertainty bounds for random d-regular graphs and provide numerically efficient upper and lower approximations for the uncertainty bound on an arbitrary graph.