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Observe Locally, Classify Globally: Using GNNs to Identify Sparse Matrix
  Structure

Observe Locally, Classify Globally: Using GNNs to Identify Sparse Matrix Structure

26 July 2023
Khaled Abdelaal
Richard Veras
ArXiv (abs)PDFHTML

Papers citing "Observe Locally, Classify Globally: Using GNNs to Identify Sparse Matrix Structure"

6 / 6 papers shown
Title
AlphaSparse: Generating High Performance SpMV Codes Directly from Sparse
  Matrices
AlphaSparse: Generating High Performance SpMV Codes Directly from Sparse Matrices
Zhen Du
Jiajia Li
Yinshan Wang
Xueqi Li
Guangming Tan
N. Sun
32
22
0
07 Nov 2022
On Positional and Structural Node Features for Graph Neural Networks on
  Non-attributed Graphs
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Hejie Cui
Zijie Lu
Pan Li
Carl Yang
60
85
0
03 Jul 2021
Fast Graph Representation Learning with PyTorch Geometric
Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey
J. E. Lenssen
3DHGNN3DPC
229
4,341
0
06 Mar 2019
A simple yet effective baseline for non-attributed graph classification
A simple yet effective baseline for non-attributed graph classification
Chen Cai
Yusu Wang
156
33
0
08 Nov 2018
How Powerful are Graph Neural Networks?
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
243
7,681
0
01 Oct 2018
Kronecker Graphs: An Approach to Modeling Networks
Kronecker Graphs: An Approach to Modeling Networks
J. Leskovec
Deepayan Chakrabarti
Jon M. Kleinberg
Christos Faloutsos
Zoubin Ghahramani
105
1,075
0
29 Dec 2008
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