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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

8 May 2023
Cai Zhou
Xiyuan Wang
Muhan Zhang
ArXivPDFHTML

Papers citing "From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks"

12 / 12 papers shown
Title
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?
X. Wang
Y. Liu
Lexi Pang
Siwei Chen
Muhan Zhang
DiffM
89
0
0
04 Feb 2025
Towards Stable, Globally Expressive Graph Representations with Laplacian
  Eigenvectors
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
Junru Zhou
Cai Zhou
Xiyuan Wang
Pan Li
Muhan Zhang
30
0
0
13 Oct 2024
Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism
  Counting Perspective
Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective
Junru Zhou
Muhan Zhang
18
0
0
04 Oct 2024
Foundations and Frontiers of Graph Learning Theory
Foundations and Frontiers of Graph Learning Theory
Yu Huang
Min Zhou
Menglin Yang
Zhen Wang
Muhan Zhang
Jie Wang
Hong Xie
Hao Wang
Defu Lian
Enhong Chen
AI4CE
GNN
43
2
0
03 Jul 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
22
7
0
04 Apr 2024
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
Sukhdeep Singh
Anuj Sharma
Vinod Kumar Chauhan
18
0
0
22 Mar 2024
Efficient Subgraph GNNs by Learning Effective Selection Policies
Efficient Subgraph GNNs by Learning Effective Selection Policies
Beatrice Bevilacqua
Moshe Eliasof
E. Meirom
Bruno Ribeiro
Haggai Maron
18
13
0
30 Oct 2023
Extending the Design Space of Graph Neural Networks by Rethinking
  Folklore Weisfeiler-Lehman
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
Jiarui Feng
Lecheng Kong
Hao Liu
Dacheng Tao
Fuhai Li
Muhan Zhang
Yixin Chen
36
10
0
05 Jun 2023
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
A Theoretical Comparison of Graph Neural Network Extensions
A Theoretical Comparison of Graph Neural Network Extensions
Pál András Papp
Roger Wattenhofer
92
45
0
30 Jan 2022
Benchmarking Graph Neural Networks
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Anh Tuan Luu
T. Laurent
Yoshua Bengio
Xavier Bresson
178
907
0
02 Mar 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
231
3,202
0
24 Nov 2016
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