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What graph neural networks cannot learn: depth vs width

What graph neural networks cannot learn: depth vs width

6 July 2019
Andreas Loukas
    GNN
ArXivPDFHTML

Papers citing "What graph neural networks cannot learn: depth vs width"

16 / 66 papers shown
Title
Interpreting and Unifying Graph Neural Networks with An Optimization
  Framework
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Meiqi Zhu
Xiao Wang
C. Shi
Houye Ji
Peng Cui
AI4CE
52
197
0
28 Jan 2021
Graph Neural Networks: Taxonomy, Advances and Trends
Graph Neural Networks: Taxonomy, Advances and Trends
Yu Zhou
Haixia Zheng
Xin Huang
Shufeng Hao
Dengao Li
Jumin Zhao
AI4TS
25
115
0
16 Dec 2020
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Dongsheng Luo
Wei Cheng
Wenchao Yu
Bo Zong
Jingchao Ni
Haifeng Chen
Xiang Zhang
OOD
16
257
0
13 Nov 2020
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node
  Representation Learning
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
Muhan Zhang
Pan Li
Yinglong Xia
Kai Wang
Long Jin
19
186
0
30 Oct 2020
Expressive Power of Invariant and Equivariant Graph Neural Networks
Expressive Power of Invariant and Equivariant Graph Neural Networks
Waïss Azizian
Marc Lelarge
25
111
0
28 Jun 2020
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Matthias Fey
Jan-Gin Yuen
F. Weichert
GNN
33
86
0
22 Jun 2020
Improving Graph Neural Network Expressivity via Subgraph Isomorphism
  Counting
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
Giorgos Bouritsas
Fabrizio Frasca
S. Zafeiriou
M. Bronstein
55
424
0
16 Jun 2020
How hard is to distinguish graphs with graph neural networks?
How hard is to distinguish graphs with graph neural networks?
Andreas Loukas
GNN
25
6
0
13 May 2020
Let's Agree to Degree: Comparing Graph Convolutional Networks in the
  Message-Passing Framework
Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
Floris Geerts
Filip Mazowiecki
Guillermo A. Pérez
GNN
24
38
0
06 Apr 2020
Universal Function Approximation on Graphs
Universal Function Approximation on Graphs
Rickard Brüel-Gabrielsson
29
6
0
14 Mar 2020
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph
  Neural Networks
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen
Fanglan Chen
Lei Zhang
Taoran Ji
Kaiqun Fu
Liang Zhao
Feng Chen
Lingfei Wu
Charu Aggarwal
Chang-Tien Lu
29
45
0
27 Feb 2020
Generalization and Representational Limits of Graph Neural Networks
Generalization and Representational Limits of Graph Neural Networks
Vikas K. Garg
Stefanie Jegelka
Tommi Jaakkola
GNN
26
303
0
14 Feb 2020
Can Graph Neural Networks Count Substructures?
Can Graph Neural Networks Count Substructures?
Zhengdao Chen
Lei Chen
Soledad Villar
Joan Bruna
GNN
57
319
0
10 Feb 2020
Random Features Strengthen Graph Neural Networks
Random Features Strengthen Graph Neural Networks
Ryoma Sato
M. Yamada
H. Kashima
GNN
AAML
13
232
0
08 Feb 2020
Graph Neural Networks: A Review of Methods and Applications
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou
Ganqu Cui
Shengding Hu
Zhengyan Zhang
Cheng Yang
Zhiyuan Liu
Lifeng Wang
Changcheng Li
Maosong Sun
AI4CE
GNN
28
5,396
0
20 Dec 2018
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CE
OCL
PINN
GNN
280
1,400
0
01 Dec 2016
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