A Directed Graph Fourier Transform with Spread Frequency Components

TL;DR

This paper introduces a novel directed graph Fourier transform (DGFT) that maximizes frequency spread in the spectral domain, enabling better analysis of signals on directed networks through an optimization-based approach and practical algorithms.

Contribution

It proposes a new DGFT construction method that maximizes spectral spread using non-convex optimization and greedy algorithms, with a heuristic from undirected Laplacian eigenvectors.

Findings

Effective DGFT basis obtained via optimization and greedy algorithms.

Improved graph signal analysis demonstrated on synthetic and real data.

Successful application to denoising temperature signals across the US.

Abstract

We study the problem of constructing a graph Fourier transform (GFT) for directed graphs (digraphs), which decomposes graph signals into different modes of variation with respect to the underlying network. Accordingly, to capture low, medium and high frequencies we seek a digraph (D)GFT such that the orthonormal frequency components are as spread as possible in the graph spectral domain. To that end, we advocate a two-step design whereby we: (i) find the maximum directed variation (i.e., a novel notion of frequency on a digraph) a candidate basis vector can attain; and (ii) minimize a smooth spectral dispersion function over the achievable frequency range to obtain the desired spread DGFT basis. Both steps involve non-convex, orthonormality-constrained optimization problems, which are efficiently tackled via a provably convergent, feasible optimization method on the Stiefel manifold. We also propose a heuristic to construct the DGFT basis from Laplacian eigen-vectors of an undirected version of the digraph. We show that the spectral-dispersion minimization problem can be cast as supermodular optimization over the set of candidate frequency components, whose orthonormality can be enforced via a matroid basis constraint. This motivates adopting a scalable greedy algorithm to obtain an approximate solution with quantifiable worst-case spectral dispersion. We illustrate the effectiveness of our DGFT algorithms through numerical tests on synthetic and real-world networks. We also carry out a graph-signal denoising task, whereby the DGFT basis is used to decompose and then low-pass filter temperatures recorded across the United States.