Key research themes
1. How are Paley graphs and their generalizations characterized in terms of their spectral properties and structural decompositions?
This area investigates the spectral characterization of Paley graphs and their generalizations, including determining eigenvalues of adjacency and Laplacian matrices, structural decompositions like Cartesian products, and the implications on automorphism groups and graph isomorphisms. It is crucial because spectral properties relate deeply to combinatorial features and symmetry, informing both graph theory and applications in coding theory and cryptography.
2. What are the spectral and combinatorial criteria to distinguish cospectral graphs, especially in contexts related to Paley graphs?
This theme focuses on spectral graph theory methods, including generalized adjacency matrices and p-adic number techniques, to distinguish non-isomorphic cospectral graphs. Since Paley graphs often have spectra that coincide with other constructions, refining spectral invariants is essential for graph isomorphism problems, with applications to automorphism group identification and graph reconstruction.
3. How can graph signal processing techniques such as blue-noise sampling be adapted and applied to graphs including Paley graphs?
This research explores the adaptation of classical sampling techniques, particularly blue-noise sampling characterized by vertex distributions maximizing inter-sample distance and spectral properties, to graph-structured data. Such techniques improve sampling and reconstruction of bandlimited signals on irregular domains, with foundational implications for data analysis on graphs like Paley graphs and related discrete structures.