Year: 2026
Type: article
Abstract: We propose graph canonical coherence analysis (gCChA), a novel framework that extends canonical correlation analysis to multivariate graph signals in the graph frequency domain. The proposed method addresses challenges posed by the inherent features of graphs: discreteness, finiteness, and irregularity. It identifies pairs of canonical graph signals that maximize their coherence, enabling the exploration of relationships between two sets of graph signals from a spectral perspective. This framework shows how these relationships change across different structural scales of the graph. We demonstrate the usefulness of this method through a simulation study and an application to economic and energy datasets of G20 countries.
Source: The American Statistician
Authors: Kyusoon Kim, Hee-Seok Oh
Institutions: Soongsil University, Seoul National University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G3285623891, G960853366
OpenAlex ID: W7124258656