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Weisfeiler and Lehman Go Cellular: CW Networks

Weisfeiler and Lehman Go Cellular: CW Networks

23 June 2021
Cristian Bodnar
Fabrizio Frasca
N. Otter
Yu Guang Wang
Pietro Lió
Guido Montúfar
M. Bronstein
    GNN
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Papers citing "Weisfeiler and Lehman Go Cellular: CW Networks"

37 / 37 papers shown
Title
SFi-Former: Sparse Flow Induced Attention for Graph Transformer
SFi-Former: Sparse Flow Induced Attention for Graph Transformer
Z. Li
J. Q. Shi
X. Zhang
Miao Zhang
B. Li
44
0
0
29 Apr 2025
Enhancing Graph Representation Learning with Localized Topological Features
Enhancing Graph Representation Learning with Localized Topological Features
Zuoyu Yan
Qi Zhao
Ze Ye
Tengfei Ma
Liangcai Gao
Zhi Tang
Yusu Wang
Chao Chen
47
0
0
17 Jan 2025
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
M. Lecha
Andrea Cavallo
Francesca Dominici
Elvin Isufi
Claudio Battiloro
AI4CE
151
2
0
17 Jan 2025
TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks
TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks
Mathilde Papillon
Guillermo Bernardez
Claudio Battiloro
Nina Miolane
BDL
49
1
0
09 Oct 2024
E(n) Equivariant Topological Neural Networks
E(n) Equivariant Topological Neural Networks
Claudio Battiloro
Ege Karaismailoglu
Mauricio Tec
George Dasoulas
Michelle Audirac
Francesca Dominici
47
4
0
24 May 2024
On the Theoretical Expressive Power and the Design Space of Higher-Order
  Graph Transformers
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
Cai Zhou
Rose Yu
Yusu Wang
27
7
0
04 Apr 2024
Contextualized Messages Boost Graph Representations
Contextualized Messages Boost Graph Representations
Brian Godwin Lim
Galvin Brice Lim
Renzo Roel Tan
Kazushi Ikeda
AI4CE
62
1
0
19 Mar 2024
Uncovering Neural Scaling Laws in Molecular Representation Learning
Uncovering Neural Scaling Laws in Molecular Representation Learning
Dingshuo Chen
Yanqiao Zhu
Jieyu Zhang
Yuanqi Du
Zhixun Li
Qiang Liu
Shu Wu
Liang Wang
21
15
0
15 Sep 2023
QDC: Quantum Diffusion Convolution Kernels on Graphs
QDC: Quantum Diffusion Convolution Kernels on Graphs
Thomas Markovich
GNN
10
3
0
20 Jul 2023
Expectation-Complete Graph Representations with Homomorphisms
Expectation-Complete Graph Representations with Homomorphisms
Pascal Welke
Maximilian Thiessen
Fabian Jogl
Thomas Gärtner
13
5
0
09 Jun 2023
From Relational Pooling to Subgraph GNNs: A Universal Framework for More
  Expressive Graph Neural Networks
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
Cai Zhou
Xiyuan Wang
Muhan Zhang
21
14
0
08 May 2023
Graph Positional Encoding via Random Feature Propagation
Graph Positional Encoding via Random Feature Propagation
Moshe Eliasof
Fabrizio Frasca
Beatrice Bevilacqua
Eran Treister
Gal Chechik
Haggai Maron
14
16
0
06 Mar 2023
A Generalization of ViT/MLP-Mixer to Graphs
A Generalization of ViT/MLP-Mixer to Graphs
Xiaoxin He
Bryan Hooi
T. Laurent
Adam Perold
Yann LeCun
Xavier Bresson
22
88
0
27 Dec 2022
On the Ability of Graph Neural Networks to Model Interactions Between
  Vertices
On the Ability of Graph Neural Networks to Model Interactions Between Vertices
Noam Razin
Tom Verbin
Nadav Cohen
19
10
0
29 Nov 2022
Beyond 1-WL with Local Ego-Network Encodings
Beyond 1-WL with Local Ego-Network Encodings
Nurudin Alvarez-Gonzalez
Andreas Kaltenbrunner
Vicencc Gómez
23
5
0
27 Nov 2022
Boosting the Cycle Counting Power of Graph Neural Networks with
  I$^2$-GNNs
Boosting the Cycle Counting Power of Graph Neural Networks with I2^22-GNNs
Yinan Huang
Xingang Peng
Jianzhu Ma
Muhan Zhang
76
46
0
22 Oct 2022
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of
  Graph Neural Networks for Attributed and Dynamic Graphs
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
19
9
0
08 Oct 2022
Expander Graph Propagation
Expander Graph Propagation
Andreea Deac
Marc Lackenby
Petar Velivcković
96
51
0
06 Oct 2022
Multimodal learning with graphs
Multimodal learning with graphs
Yasha Ektefaie
George Dasoulas
Ayush Noori
Maha Farhat
Marinka Zitnik
38
82
0
07 Sep 2022
Lower and Upper Bounds for Numbers of Linear Regions of Graph
  Convolutional Networks
Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks
Hao Chen
Yu Wang
Huan Xiong
GNN
8
6
0
01 Jun 2022
Recipe for a General, Powerful, Scalable Graph Transformer
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek
Mikhail Galkin
Vijay Prakash Dwivedi
A. Luu
Guy Wolf
Dominique Beaini
43
507
0
25 May 2022
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
Christopher Morris
Gaurav Rattan
Sandra Kiefer
Siamak Ravanbakhsh
33
39
0
25 Mar 2022
Sign and Basis Invariant Networks for Spectral Graph Representation
  Learning
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Derek Lim
Joshua Robinson
Lingxiao Zhao
Tess E. Smidt
S. Sra
Haggai Maron
Stefanie Jegelka
20
138
0
25 Feb 2022
Structure-Aware Transformer for Graph Representation Learning
Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen
Leslie O’Bray
Karsten M. Borgwardt
26
237
0
07 Feb 2022
A Theoretical Comparison of Graph Neural Network Extensions
A Theoretical Comparison of Graph Neural Network Extensions
Pál András Papp
Roger Wattenhofer
95
45
0
30 Jan 2022
Simplicial Convolutional Filters
Simplicial Convolutional Filters
Maosheng Yang
Elvin Isufi
Michael T. Schaub
G. Leus
25
31
0
27 Jan 2022
Dist2Cycle: A Simplicial Neural Network for Homology Localization
Dist2Cycle: A Simplicial Neural Network for Homology Localization
A. Keros
Vidit Nanda
Kartic Subr
19
28
0
28 Oct 2021
Equivariant Subgraph Aggregation Networks
Equivariant Subgraph Aggregation Networks
Beatrice Bevilacqua
Fabrizio Frasca
Derek Lim
Balasubramaniam Srinivasan
Chen Cai
G. Balamurugan
M. Bronstein
Haggai Maron
22
174
0
06 Oct 2021
Graph Neural Networks for Graph Drawing
Graph Neural Networks for Graph Drawing
Matteo Tiezzi
Gabriele Ciravegna
Marco Gori
21
20
0
21 Sep 2021
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Xuebin Zheng
Bingxin Zhou
Yu Guang Wang
Xiaosheng Zhuang
27
34
0
12 Dec 2020
A Survey on The Expressive Power of Graph Neural Networks
A Survey on The Expressive Power of Graph Neural Networks
Ryoma Sato
167
170
0
09 Mar 2020
Benchmarking Graph Neural Networks
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Anh Tuan Luu
T. Laurent
Yoshua Bengio
Xavier Bresson
183
913
0
02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
229
1,935
0
09 Jun 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph
  Generation
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You
Bowen Liu
Rex Ying
Vijay S. Pande
J. Leskovec
GNN
184
884
0
07 Jun 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
219
1,329
0
12 Feb 2018
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti
Davide Boscaini
Jonathan Masci
Emanuele Rodolà
Jan Svoboda
M. Bronstein
GNN
234
1,809
0
25 Nov 2016
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
231
3,230
0
24 Nov 2016
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