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1907.03199
Cited By
What graph neural networks cannot learn: depth vs width
6 July 2019
Andreas Loukas
GNN
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Papers citing
"What graph neural networks cannot learn: depth vs width"
50 / 66 papers shown
Title
Towards Invariance to Node Identifiers in Graph Neural Networks
Maya Bechler-Speicher
Moshe Eliasof
Carola-Bibiane Schönlieb
Ran Gilad-Bachrach
Amir Globerson
65
1
0
20 Feb 2025
Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction
Giuseppe Alessio DÍnverno
Saeid Moradizadeh
Sajad Salavatidezfouli
Pasquale Claudio Africa
G. Rozza
AI4CE
38
0
0
04 Oct 2024
Revisiting Random Walks for Learning on Graphs
Jinwoo Kim
Olga Zaghen
Ayhan Suleymanzade
Youngmin Ryou
Seunghoon Hong
59
0
0
01 Jul 2024
Lightweight Spatial Modeling for Combinatorial Information Extraction From Documents
Yanfei Dong
Lambert Deng
Jiazheng Zhang
Xiaodong Yu
Ting Lin
Francesco Gelli
Soujanya Poria
W. Lee
40
0
0
08 May 2024
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
Raffaele Marino
L. Buffoni
Bogdan Zavalnij
GNN
40
5
0
13 Mar 2024
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning
Hanxuan Yang
Qingchao Kong
Wenji Mao
BDL
20
0
0
09 Dec 2023
Going beyond persistent homology using persistent homology
Johanna Immonen
Amauri H. Souza
Vikas K. Garg
38
9
0
10 Nov 2023
The Expressive Power of Graph Neural Networks: A Survey
Bingxue Zhang
Changjun Fan
Shixuan Liu
Kuihua Huang
Xiang Zhao
Jin-Yu Huang
Zhong Liu
40
19
0
16 Aug 2023
Weisfeiler and Leman Go Measurement Modeling: Probing the Validity of the WL Test
Arjun Subramonian
Adina Williams
Maximilian Nickel
Yizhou Sun
Levent Sagun
28
0
0
11 Jul 2023
Graph Inductive Biases in Transformers without Message Passing
Liheng Ma
Chen Lin
Derek Lim
Adriana Romero Soriano
P. Dokania
Mark J. Coates
Philip H. S. Torr
Ser-Nam Lim
AI4CE
31
85
0
27 May 2023
Descriptive complexity for distributed computing with circuits
Veeti Ahvonen
Damian Heiman
L. Hella
Antti Kuusisto
24
4
0
08 Mar 2023
Equivariant Polynomials for Graph Neural Networks
Omri Puny
Derek Lim
B. Kiani
Haggai Maron
Y. Lipman
28
31
0
22 Feb 2023
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
Ryoma Sato
34
5
0
26 Jan 2023
Everything is Connected: Graph Neural Networks
Petar Velickovic
GNN
AI4CE
22
179
0
19 Jan 2023
A Generalization of ViT/MLP-Mixer to Graphs
Xiaoxin He
Bryan Hooi
T. Laurent
Adam Perold
Yann LeCun
Xavier Bresson
47
88
0
27 Dec 2022
On the Ability of Graph Neural Networks to Model Interactions Between Vertices
Noam Razin
Tom Verbin
Nadav Cohen
23
10
0
29 Nov 2022
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Anders Aamand
Justin Y. Chen
Piotr Indyk
Shyam Narayanan
R. Rubinfeld
Nicholas Schiefer
Sandeep Silwal
Tal Wagner
39
21
0
06 Nov 2022
Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Network
Yi Yi
Xu Wan
Kangfei Zhao
Ou-Yang Le
Pei-Ying Zhao
21
1
0
27 Oct 2022
Boosting the Cycle Counting Power of Graph Neural Networks with I
2
^2
2
-GNNs
Yinan Huang
Xingang Peng
Jianzhu Ma
Muhan Zhang
84
47
0
22 Oct 2022
Low-Rank Representations Towards Classification Problem of Complex Networks
Murat Çelik
Ali Baran Tasdemir
Lale Özkahya
GNN
11
0
0
20 Oct 2022
On Classification Thresholds for Graph Attention with Edge Features
K. Fountoulakis
Dake He
Silvio Lattanzi
Bryan Perozzi
Anton Tsitsulin
Shenghao Yang
GNN
30
6
0
18 Oct 2022
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
Silvia Beddar-Wiesing
Giuseppe Alessio D’Inverno
C. Graziani
Veronica Lachi
Alice Moallemy-Oureh
F. Scarselli
J. M. Thomas
31
9
0
08 Oct 2022
Provably expressive temporal graph networks
Amauri Souza
Diego Mesquita
Samuel Kaski
Vikas K. Garg
89
54
0
29 Sep 2022
On Representing Linear Programs by Graph Neural Networks
Ziang Chen
Jialin Liu
Xinshang Wang
Jian Lu
W. Yin
AI4CE
60
31
0
25 Sep 2022
On the Privacy Risks of Cell-Based NAS Architectures
Haiping Huang
Zhikun Zhang
Yun Shen
Michael Backes
Qi Li
Yang Zhang
27
7
0
04 Sep 2022
Agent-based Graph Neural Networks
Karolis Martinkus
Pál András Papp
Benedikt Schesch
Roger Wattenhofer
LLMAG
GNN
29
17
0
22 Jun 2022
Universally Expressive Communication in Multi-Agent Reinforcement Learning
Matthew Morris
Thomas D. Barrett
Arnu Pretorius
24
4
0
14 Jun 2022
Shortest Path Networks for Graph Property Prediction
Ralph Abboud
Radoslav Dimitrov
.Ismail .Ilkan Ceylan
GNN
27
45
0
02 Jun 2022
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek
Mikhail Galkin
Vijay Prakash Dwivedi
A. Luu
Guy Wolf
Dominique Beaini
57
515
0
25 May 2022
Not too little, not too much: a theoretical analysis of graph (over)smoothing
Nicolas Keriven
41
88
0
24 May 2022
Theory of Graph Neural Networks: Representation and Learning
Stefanie Jegelka
GNN
AI4CE
33
68
0
16 Apr 2022
Graph Neural Networks for Wireless Communications: From Theory to Practice
Yifei Shen
Jun Zhang
Shenghui Song
Khaled B. Letaief
GNN
AI4CE
25
110
0
21 Mar 2022
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
Yu Shi
Shuxin Zheng
Guolin Ke
Yifei Shen
Jiacheng You
Jiyan He
Shengjie Luo
Chang-Shu Liu
Di He
Tie-Yan Liu
AI4CE
42
65
0
09 Mar 2022
A Theoretical Comparison of Graph Neural Network Extensions
Pál András Papp
Roger Wattenhofer
100
46
0
30 Jan 2022
A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants
Ningyuan Huang
Soledad Villar
24
62
0
18 Jan 2022
Equivariant Quantum Graph Circuits
Péter Mernyei
K. Meichanetzidis
.Ismail .Ilkan Ceylan
36
8
0
10 Dec 2021
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
Haoyang Li
Xin Wang
Ziwei Zhang
Wenwu Zhu
OODD
OOD
26
97
0
07 Dec 2021
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition
H. Sahbi
GNN
23
27
0
06 Dec 2021
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
Pál András Papp
Karolis Martinkus
Lukas Faber
Roger Wattenhofer
GNN
22
138
0
11 Nov 2021
On Provable Benefits of Depth in Training Graph Convolutional Networks
Weilin Cong
M. Ramezani
M. Mahdavi
24
73
0
28 Oct 2021
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Mucong Ding
Kezhi Kong
Jingling Li
Chen Zhu
John P. Dickerson
Furong Huang
Tom Goldstein
GNN
MQ
33
47
0
27 Oct 2021
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
Lingxiao Zhao
Wei Jin
L. Akoglu
Neil Shah
GNN
24
160
0
07 Oct 2021
Equivariant Subgraph Aggregation Networks
Beatrice Bevilacqua
Fabrizio Frasca
Derek Lim
Balasubramaniam Srinivasan
Chen Cai
G. Balamurugan
M. Bronstein
Haggai Maron
53
175
0
06 Oct 2021
Graph Neural Networks: Methods, Applications, and Opportunities
Lilapati Waikhom
Ripon Patgiri
GNN
34
42
0
24 Aug 2021
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Zhiqian Chen
Fanglan Chen
Lei Zhang
Taoran Ji
Kaiqun Fu
Liang Zhao
Feng Chen
Lingfei Wu
Charu C. Aggarwal
Chang-Tien Lu
38
18
0
21 Jul 2021
Graph Neural Networks with Local Graph Parameters
Pablo Barceló
Floris Geerts
Juan L. Reutter
Maksimilian Ryschkov
24
65
0
12 Jun 2021
Scalars are universal: Equivariant machine learning, structured like classical physics
Soledad Villar
D. Hogg
Kate Storey-Fisher
Weichi Yao
Ben Blum-Smith
PINN
AI4CE
24
130
0
11 Jun 2021
Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks
H. Sahbi
3DH
GNN
18
3
0
09 Apr 2021
Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart
Didier Chételat
Elias Boutros Khalil
Andrea Lodi
Christopher Morris
Petar Velickovic
AI4CE
32
347
0
18 Feb 2021
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Aseem Baranwal
K. Fountoulakis
Aukosh Jagannath
OODD
39
75
0
13 Feb 2021
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