Graph coarsening: from scientific computing to machine learning
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...
Main Authors: | Chen, Jie, Saad, Yousef, Zhang, Zechen |
---|---|
Other Authors: | MIT-IBM Watson AI Lab |
Format: | Article |
Language: | English |
Published: |
Springer International Publishing
2022
|
Online Access: | https://hdl.handle.net/1721.1/139626 |
Similar Items
-
Hierarchical and Unsupervised Graph Representation Learning with Loukas’s Coarsening
by: Louis Béthune, et al.
Published: (2020-08-01) -
Probabilistic Coarsening for Knowledge Graph Embeddings
by: Marcin Pietrasik, et al.
Published: (2023-03-01) -
Defining coarsenings of valuations
by: Jahnke, F, et al.
Published: (2017) -
DOMAIN GROWTH AND COARSENING
by: Bray, A
Published: (1993) -
Global stability and bounds for coarsening rates within the mean-field theory for domain coarsening
by: Niethammer, B, et al.
Published: (2006)