# Low-Bit Quantization of Bandlimited Graph Signals via Iterative Methods

arXiv: https://arxiv.org/abs/2601.18782 · HTML: https://mlsanity.com/paper/2601.18782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [An Introduction to Matrix Concentration Inequalities](https://mlsanity.com/paper/1501.01571/) (2015)
- [Adaptive Graph Encoder for Attributed Graph Embedding](https://mlsanity.com/paper/2007.01594/) (2020)
- [Vertex-Frequency Analysis on Graphs](https://mlsanity.com/paper/1307.5708/) (2013)
- [A Greedy Algorithm for Quantizing Neural Networks](https://mlsanity.com/paper/2010.15979/) (2020)
- [A simple approach for quantizing neural networks](https://mlsanity.com/paper/2209.03487/) (2022)
- [Approximation of functions with one-bit neural networks](https://mlsanity.com/paper/2112.09181/) (2021)
- [Sigma Delta quantization for images](https://mlsanity.com/paper/2005.08487/) (2020)
- [Enhanced Digital Halftoning via Weighted Sigma-Delta Modulation](https://mlsanity.com/paper/2202.04986/) (2022)
- [The Mathematics of Dots and Pixels: On the Theoretical Foundations of Image Halftoning](https://mlsanity.com/paper/2406.12760/) (2024)

## Abstract (arXiv)

We study the quantization of real-valued bandlimited signals on graphs, focusing on low-bit representations. We propose iterative noise-shaping algorithms for quantization, including sampling approaches with and without vertex replacement. The methods leverage the spectral properties of the graph Laplacian and exploit graph incoherence to achieve high-fidelity approximations. Theoretical guarantees are provided for the random sampling method, and extensive numerical experiments on synthetic and real-world graphs illustrate the efficiency and robustness of the proposed schemes.