mlsanityData to 14 Oct 2026

Low-Bit Quantization of Bandlimited Graph Signals via Iterative Methods

arXiv 2601.18782 ↗First posted 26 Jan 2026eess.SP cs.NA eess.IVcited by 0

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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.

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.

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References · 9

  1. 2015An Introduction to Matrix Concentration Inequalitiescited by 397
  2. 2020Adaptive Graph Encoder for Attributed Graph Embeddingcited by 89
  3. 2013Vertex-Frequency Analysis on Graphscited by 25
  4. 2020A Greedy Algorithm for Quantizing Neural Networkscited by 22
  5. 2022A simple approach for quantizing neural networkscited by 8
  6. 2021Approximation of functions with one-bit neural networkscited by 5
  7. 2020Sigma Delta quantization for imagescited by 2
  8. 2022Enhanced Digital Halftoning via Weighted Sigma-Delta Modulationcited by 2
  9. 2024The Mathematics of Dots and Pixels: On the Theoretical Foundations of Image Halftoningcited by 1

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  5. 2023Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation
  6. 2026Graph Residual Conjugate Diffusion: SNR-Equalized Heat Flow for Graph Signals
  7. 2025Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
  8. 2026Lossy compression of weighted graph adjacency matrices by transform coding ↗