Graph coarsening: From scientific computing to machine learning

TL;DR

This paper reviews how graph coarsening techniques, historically used in scientific computing, are increasingly applied in machine learning to simplify graphs while preserving their essential properties.

Contribution

It provides a broad overview of graph coarsening methods from scientific computing and explores their emerging applications in machine learning.

Findings

Graph coarsening is crucial in multilevel algorithms in scientific computing.

In machine learning, coarsening helps reduce graph size while maintaining structure.

Spectral properties are commonly used to guide coarsening processes.

Abstract

The general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning. The goal of this paper is to take a broad look into coarsening techniques that have been successfully deployed in scientific computing and see how similar principles are finding their way in more recent applications related to machine learning. In scientific computing, coarsening plays a central role in algebraic multigrid methods as well as the related class of multilevel incomplete LU factorizations. In machine learning, graph coarsening goes under various names, e.g., graph downsampling or graph reduction. Its goal in most cases is to replace some original graph by one which has fewer nodes, but whose structure and characteristics are similar to those of the original graph. As will be seen, a common strategy in these methods is to rely on spectral properties to define the coarse graph.