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Limits, approximation and size transferability for GNNs on sparse graphs
  via graphops

Limits, approximation and size transferability for GNNs on sparse graphs via graphops

7 June 2023
Thien Le
Stefanie Jegelka
ArXivPDFHTML

Papers citing "Limits, approximation and size transferability for GNNs on sparse graphs via graphops"

15 / 15 papers shown
Title
Graph neural networks extrapolate out-of-distribution for shortest paths
Graph neural networks extrapolate out-of-distribution for shortest paths
Robert Nerem
Samantha Chen
Sanjoy Dasgupta
Yusu Wang
44
0
0
24 Mar 2025
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing
Lukas Fesser
Melanie Weber
36
0
0
01 Mar 2025
Graph neural networks and non-commuting operators
Graph neural networks and non-commuting operators
Mauricio Velasco
Kaiying O'Hare
Bernardo Rychtenberg
Soledad Villar
GNN
127
1
0
06 Nov 2024
The Transferability of Downsamped Sparse Graph Convolutional Networks
The Transferability of Downsamped Sparse Graph Convolutional Networks
Qinji Shu
Hang Sheng
Feng Ji
Hui Feng
Bo Hu
BDL
26
0
0
30 Aug 2024
Generalization of Graph Neural Networks is Robust to Model Mismatch
Generalization of Graph Neural Networks is Robust to Model Mismatch
Zhiyang Wang
J. Cerviño
Alejandro Ribeiro
45
1
0
25 Aug 2024
A Manifold Perspective on the Statistical Generalization of Graph Neural
  Networks
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
Zhiyang Wang
J. Cerviño
Alejandro Ribeiro
AI4CE
GNN
36
6
0
07 Jun 2024
A Diffusion Model Framework for Unsupervised Neural Combinatorial
  Optimization
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Sebastian Sanokowski
Sepp Hochreiter
Sebastian Lehner
29
17
0
03 Jun 2024
Generalization Bounds for Message Passing Networks on Mixture of
  Graphons
Generalization Bounds for Message Passing Networks on Mixture of Graphons
Sohir Maskey
Gitta Kutyniok
Ron Levie
59
6
0
04 Apr 2024
Future Directions in the Theory of Graph Machine Learning
Future Directions in the Theory of Graph Machine Learning
Christopher Morris
Fabrizio Frasca
Nadav Dym
Haggai Maron
.Ismail .Ilkan Ceylan
Ron Levie
Derek Lim
Michael M. Bronstein
Martin Grohe
Stefanie Jegelka
AI4CE
32
5
0
03 Feb 2024
A Poincaré Inequality and Consistency Results for Signal Sampling on
  Large Graphs
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Thien Le
Luana Ruiz
Stefanie Jegelka
29
0
0
17 Nov 2023
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph
  Neural Networks
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
Ching-Yao Chuang
Stefanie Jegelka
OOD
67
35
0
04 Oct 2022
Generalization Analysis of Message Passing Neural Networks on Large
  Random Graphs
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
Sohir Maskey
Ron Levie
Yunseok Lee
Gitta Kutyniok
GNN
80
54
0
01 Feb 2022
Transferability of Graph Neural Networks: an Extended Graphon Approach
Transferability of Graph Neural Networks: an Extended Graphon Approach
Sohir Maskey
Ron Levie
Gitta Kutyniok
GNN
91
50
0
21 Sep 2021
From Local Structures to Size Generalization in Graph Neural Networks
From Local Structures to Size Generalization in Graph Neural Networks
Gilad Yehudai
Ethan Fetaya
E. Meirom
Gal Chechik
Haggai Maron
GNN
AI4CE
169
123
0
17 Oct 2020
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
259
3,239
0
24 Nov 2016
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