Harmonic Analysis on Directed Networks: A Biorthogonal Laplacian Framework for Non-Normal Graphs

TL;DR

This paper introduces a biorthogonal harmonic analysis framework for directed, non-normal graphs using a new directed Laplacian and dual eigenbases, enabling exact signal reconstruction and quantifying energy distortion due to non-normality.

Contribution

It develops a biorthogonal Fourier transform for directed graphs based on the combinatorial Laplacian, linking stability to the eigenvector condition number and providing a rigorous analysis of non-normality effects.

Findings

Exact reconstruction possible in ideal conditions

Energy distortion linked to eigenvector condition number

Non-normality limits filter stability in directed networks

Abstract

Classical spectral graph theory relies on the symmetry of the adjacency and Laplacian operators, which guarantees orthogonal eigenbases and energy-preserving Fourier transforms. However, real-world networks are intrinsically directed and asymmetric, resulting in non-normal operators where standard orthogonality assumptions fail. In this paper, we develop a rigorous harmonic analysis framework for directed graphs centered on the \emph{Combinatorial Directed Laplacian} ($L = D_{out} - A$). We construct a \emph{Biorthogonal Graph Fourier Transform} (BGFT) using dual left and right eigenbases, and introduce a directed variational semi-norm based on the operator norm $\|Lx\|_2$ rather than the quadratic form. We derive exact Parseval-type bounds that quantify the energy distortion induced by the non-normality of the graph, explicitly linking signal reconstruction stability to the condition number of the eigenvector matrix, $\kappa(V)$. Finally, we present experimental validation comparing normal directed cycles against non-normal perturbed topologies, demonstrating that while the BGFT provides exact reconstruction in ideal regimes, the geometric departure from normality acts as the fundamental limit on filter stability in directed networks.