Graph coarsening: from scientific computing to machine learning Article Swipe
Jie Chen
,
Yousef Saad
,
Zechen Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s40324-021-00282-x
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s40324-021-00282-x
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40324-021-00282-x
- https://link.springer.com/content/pdf/10.1007/s40324-021-00282-x.pdf
- OA Status
- hybrid
- Cited By
- 28
- References
- 130
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3175521565
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3175521565Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s40324-021-00282-xDigital Object Identifier
- Title
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Graph coarsening: from scientific computing to machine learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-01-10Full publication date if available
- Authors
-
Jie Chen, Yousef Saad, Zechen ZhangList of authors in order
- Landing page
-
https://doi.org/10.1007/s40324-021-00282-xPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s40324-021-00282-x.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s40324-021-00282-x.pdfDirect OA link when available
- Concepts
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Computer science, Graph, Theoretical computer science, Upsampling, Graph reduction, Artificial intelligence, Image (mathematics), Functional programmingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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28Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 11, 2023: 4, 2022: 5Per-year citation counts (last 5 years)
- References (count)
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130Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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