Work

Graph Signal Adaptive Message Passing

Year: 2025

Type: article

Abstract: This paper proposes Graph Signal Adaptive Message Passing (GSAMP) by employing a distinct approach that utilizes localized computations at each node based on an adaptive solution obtained from an optimization problem designed to minimize the discrepancy between observed and estimated values. This localized approach distinguishes GSAMP from conventional graph methods derived from the entire graph. GSAMP efficiently handles real-world time-varying graph signals under Gaussian and impulsive noise, performing online prediction, missing data imputation, and noise removal simultaneously.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2025.3569478

Authors: Yi Yan, Changran Peng, Ercan E. Kuruoglu

Institutions: Tsinghua Shenzhen International Graduate School, Tsinghua University

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Science, Technology and Innovation Commission of Shenzhen Municipality

Award: G6887692730