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Graph frequency-domain regression
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DOI：10.1080/02331888.2026.2675549.png)
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Abstract
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We propose a regression framework operating in the graph frequency domain to model relationships among variables observed on the vertices of a graph. The proposed model is constructed via graph filters and operates in the graph frequency domain. We derive ordinary least squares estimators for the model coefficients and establish their theoretical properties, including consistency and asymptotic normality. We further develop a statistical testing procedure for variable significance at each graph frequency and provide frequency-specific interpretations of the regression coefficients. The proposed framework serves as a foundational regression methodology for graph-indexed data. Its practical usefulness is demonstrated through a simulation study and a real data application to a trading network.
Keywords:
Frequency domain
graph signal processing
multivariate graph signal
regression
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