Abstract
SAND2024-01365O
The Graph Neural Networks and Applied Linear Algebra is companion software for the educational article with the same title. The software provides illustrative examples of graph neural networks in Matlab and Python. These stand-alone algorithms are for educational purposes.
The software also includes graph neural network-based algorithms for a trainable Jacobi iteration as well as diffusion coefficient estimation. The software provides human-interpretable implementations of Graph Neural Networks in Matlab and Python. These implementations are not optimized for performance and instead emphasize readability.
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
- Developers:
-
Tuminaro, Raymond [1][2][3] ; Siefert, Christopher [1][2][3] ; Ohm, Peter [1][2][3] ; Moore, Nicholas [1][2][3]
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Release Date:
- 2023-10-03
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
MATLAB
Python
- Version:
- 1.0
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:NA0003525
- Code ID:
- 125157
- Site Accession Number:
- SCR #2960.0
- Research Org.:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Country of Origin:
- United States
- Keywords:
- SciDAC
Citation Formats
Tuminaro, Raymond, Siefert, Christopher, Ohm, Peter, and Moore, Nicholas.
Graph Neural Networks and Applied Linear Algebra v.1.0.
Computer Software.
https://github.com/sandialabs/gnn-applied-linear-algebra.
USDOE.
03 Oct. 2023.
Web.
doi:10.11578/dc.20240319.3.
Tuminaro, Raymond, Siefert, Christopher, Ohm, Peter, & Moore, Nicholas.
(2023, October 03).
Graph Neural Networks and Applied Linear Algebra v.1.0.
[Computer software].
https://github.com/sandialabs/gnn-applied-linear-algebra.
https://doi.org/10.11578/dc.20240319.3.
Tuminaro, Raymond, Siefert, Christopher, Ohm, Peter, and Moore, Nicholas.
"Graph Neural Networks and Applied Linear Algebra v.1.0." Computer software.
October 03, 2023.
https://github.com/sandialabs/gnn-applied-linear-algebra.
https://doi.org/10.11578/dc.20240319.3.
@misc{
doecode_125157,
title = {Graph Neural Networks and Applied Linear Algebra v.1.0},
author = {Tuminaro, Raymond and Siefert, Christopher and Ohm, Peter and Moore, Nicholas},
abstractNote = {SAND2024-01365O
The Graph Neural Networks and Applied Linear Algebra is companion software for the educational article with the same title. The software provides illustrative examples of graph neural networks in Matlab and Python. These stand-alone algorithms are for educational purposes.
The software also includes graph neural network-based algorithms for a trainable Jacobi iteration as well as diffusion coefficient estimation. The software provides human-interpretable implementations of Graph Neural Networks in Matlab and Python. These implementations are not optimized for performance and instead emphasize readability.
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.},
doi = {10.11578/dc.20240319.3},
url = {https://doi.org/10.11578/dc.20240319.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240319.3}},
year = {2023},
month = {oct}
}