Abstract: This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the source of a ...

This repository provides the codebase used to study the behavior of graph Laplacian mutual coherence over Erdős–Rényi and sensor graphs. It evaluates the accuracy of the upper and lower bounds ...

The following paper by Batson, Spielman, Srivastava, and Teng surveys one of the most important recent intellectual achievements of theoretical computer science, demonstrating that every weighted ...

Official implementation of the paper "Multi-Scale Graph Learning for Anti-Sparse Downscaling" accepted at AAAI 2025. This repository contains the source code, training scripts, and evaluation tools ...

While it is important to find the key biomarkers and improve the accuracy of disease models, it is equally important to understand their interaction relationships. In this study, a transparent sparse ...

Graph sparsification is the approximation of an arbitrary graph by a sparse graph. We explain what it means for one graph to be a spectral approximation of another and review the development of ...

Abstract: Multi-view spectral clustering has achieved impressive performance by learning multiple robust and meaningful similarity graphs for clustering. Generally, the existing literatures often ...

Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be ...