Graph coarsening: from scientific computing to machine learning
Name
40324_2021_Article_282.pdf
Size
2.27 MB
Format
Adobe PDF
Checksum (MD5)
8b7e7ec59a26718a251d45388930b471
Author(s)
Chen, Jie
Saad, Yousef
Zhang, Zechen
Date Issued
January 10, 2022
Publisher
Springer International Publishing
Citation
Chen, Jie, Saad, Yousef and Zhang, Zechen. 2022. "Graph coarsening: from scientific computing to machine learning."
Version
Final published version
Abstract
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.
MIT Department
MIT-IBM Watson AI Lab
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s40324-021-00282-x
