# Graph Fourier transform based on singular value decomposition of directed Laplacian

arXiv: https://arxiv.org/abs/2205.06242 · HTML: https://mlsanity.com/paper/2205.06242/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2022.

## Built on by (most cited first, 4 of 4)

- [Barron Space for Graph Convolution Neural Networks](https://mlsanity.com/paper/2311.02838/) (2023)
- [Haar-Laplacian for directed graphs](https://mlsanity.com/paper/2411.15527/) (2024)
- [Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast](https://mlsanity.com/paper/2505.13102/) (2025)
- [A Framework for Directed Hypergraph Signal Processing via tensor t-SVD](https://mlsanity.com/paper/2606.25112/) (2026)

## Abstract (arXiv)

Graph Fourier transform (GFT) is a fundamental concept in graph signal processing. In this paper, based on singular value decomposition of Laplacian, we introduce a novel definition of GFT on directed graphs, and use singular values of Laplacian to carry the notion of graph frequencies. % of the proposed GFT. The proposed GFT is consistent with the conventional GFT in the undirected graph setting, and on directed circulant graphs, the proposed GFT is the classical discrete Fourier transform, up to some rotation, permutation and phase adjustment. We show that frequencies and frequency components of the proposed GFT can be evaluated by solving some constrained minimization problems with low computational cost. Numerical demonstrations indicate that the proposed GFT could represent graph signals with different modes of variation efficiently.