Investigating the relationship between graph eigenvector ordering and the signal processing dual

TL;DR

This paper explores the connection between graph eigenvector ordering and the concept of dual graphs in graph signal processing, proposing a measure to quantify their relationship and unifying different approaches.

Contribution

It introduces a dualness measure for graphs, develops an algorithm to compute it, and demonstrates how this unifies existing similarity metrics and duality concepts.

Findings

Structured graphs are better analyzed with similarity metrics.

Erdős–Rényi graphs are better analyzed with duality measures.

The proposed measure unifies different graph analysis techniques.

Abstract

Graph signal processing uses the graph eigenvector basis to analyze signals. However, these graph eigenvectors are typically linearly ordered (by total variation), which may not be reasonable for many graph structures. There have been structure based similarity metrics proposed in the literature that better capture the geometry of graph eigenvectors. On the other hand, there has been work that attempts to generalize the concept of duality to graph signal processing. The (signal processing) the dual graph captures the relationship between graph frequencies and is obtained by typically inverting the graph Fourier transform operation. In this work, we investigate the connections between these two concepts. We propose a dualness measure of two graphs, which quantifies how close the graphs are being (signal processing) duals of each other. We show that this definition satisfies some desirable properties and develop an algorithm based on coordinate descent and perfect matching to compute an approximation to dualness. By computing the dualness measure, we observe that for structured graphs, the similarity metric-based techniques give a better dual graph (and vice-versa for ER graphs), suggesting a potentially novel approach unifying the two methods.