ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2405.12474
  4. Cited By
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks:
  Heterophily, Over-smoothing, and Over-squashing

How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing

21 May 2024
Keke Huang
Yu Guang Wang
Ming Li
Pietro Lio
ArXivPDFHTML

Papers citing "How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing"

42 / 42 papers shown
Title
Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace
  Approach
Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach
Keke Huang
Wencai Cao
Hoang Ta
Xiaokui Xiao
Pietro Lio
103
5
0
12 Mar 2024
A Survey on Oversmoothing in Graph Neural Networks
A Survey on Oversmoothing in Graph Neural Networks
T. Konstantin Rusch
Michael M. Bronstein
Siddhartha Mishra
77
205
0
20 Mar 2023
Specformer: Spectral Graph Neural Networks Meet Transformers
Specformer: Spectral Graph Neural Networks Meet Transformers
Deyu Bo
Chuan Shi
Lele Wang
Renjie Liao
104
84
0
02 Mar 2023
Graph Neural Networks with Learnable and Optimal Polynomial Bases
Graph Neural Networks with Learnable and Optimal Polynomial Bases
Y. Guo
Zhewei Wei
63
32
0
24 Feb 2023
A Survey on Spectral Graph Neural Networks
A Survey on Spectral Graph Neural Networks
Deyu Bo
Xiao Wang
Yang Liu
Yuan Fang
Yawen Li
Chuan Shi
66
27
0
11 Feb 2023
On Over-Squashing in Message Passing Neural Networks: The Impact of
  Width, Depth, and Topology
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Francesco Di Giovanni
Lorenzo Giusti
Federico Barbero
Giulia Luise
Pietro Lio
Michael M. Bronstein
86
117
0
06 Feb 2023
Ordered GNN: Ordering Message Passing to Deal with Heterophily and
  Over-smoothing
Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing
Yunchong Song
Cheng Zhou
Xinbing Wang
Zhouhan Lin
66
69
0
03 Feb 2023
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci
  Curvature
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature
K. Nguyen
Hieu Nong
T. Nguyen
Nhat Ho
Khuong N. Nguyen
Vinh Phu Nguyen
53
66
0
28 Nov 2022
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Kedar Karhadkar
P. Banerjee
Guido Montúfar
68
63
0
21 Oct 2022
Revisiting Heterophily For Graph Neural Networks
Revisiting Heterophily For Graph Neural Networks
Sitao Luan
Chenqing Hua
Qincheng Lu
Jiaqi Zhu
Mingde Zhao
Shuyuan Zhang
Xiaoming Chang
Doina Precup
69
193
0
14 Oct 2022
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural
  Networks
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
Runlin Lei
Zhen Wang
Yaliang Li
Bolin Ding
Zhewei Wei
AAML
70
44
0
27 May 2022
How Powerful are Spectral Graph Neural Networks
How Powerful are Spectral Graph Neural Networks
Xiyuan Wang
Muhan Zhang
99
195
0
23 May 2022
Finding Global Homophily in Graph Neural Networks When Meeting
  Heterophily
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li
Renyu Zhu
Yao Cheng
Caihua Shan
Siqiang Luo
Dongsheng Li
Wei Qian
54
191
0
15 May 2022
Simplified Graph Convolution with Heterophily
Simplified Graph Convolution with Heterophily
Sudhanshu Chanpuriya
Cameron Musco
52
24
0
08 Feb 2022
Convolutional Neural Networks on Graphs with Chebyshev Approximation,
  Revisited
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
Mingguo He
Zhewei Wei
Ji-Rong Wen
GNN
53
107
0
04 Feb 2022
Understanding over-squashing and bottlenecks on graphs via curvature
Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping
Francesco Di Giovanni
B. Chamberlain
Xiaowen Dong
M. Bronstein
101
442
0
29 Nov 2021
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and
  Strong Simple Methods
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Derek Lim
Felix Hohne
Xiuyu Li
Sijia Huang
Vaishnavi Gupta
Omkar Bhalerao
Ser-Nam Lim
107
354
0
27 Oct 2021
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Kaixiong Zhou
Xiao Shi Huang
Daochen Zha
Rui Chen
Li Li
Soo-Hyun Choi
Xia Hu
GNN
AI4CE
59
116
0
06 Jul 2021
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein
  Approximation
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Mingguo He
Zhewei Wei
Zengfeng Huang
Hongteng Xu
106
226
0
21 Jun 2021
Breaking the Limit of Graph Neural Networks by Improving the
  Assortativity of Graphs with Local Mixing Patterns
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Susheel Suresh
Vinith Budde
Jennifer Neville
Pan Li
Jianzhu Ma
63
135
0
11 Jun 2021
How Framelets Enhance Graph Neural Networks
How Framelets Enhance Graph Neural Networks
Xuebin Zheng
Bingxin Zhou
Junbin Gao
Yu Guang Wang
Pietro Lio
Ming Li
Guido Montúfar
72
69
0
13 Feb 2021
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
91
202
0
28 Jan 2021
Simple and Deep Graph Convolutional Networks
Simple and Deep Graph Convolutional Networks
Ming Chen
Zhewei Wei
Zengfeng Huang
Bolin Ding
Yaliang Li
GNN
102
1,483
0
04 Jul 2020
Adaptive Universal Generalized PageRank Graph Neural Network
Adaptive Universal Generalized PageRank Graph Neural Network
Eli Chien
Jianhao Peng
Pan Li
O. Milenkovic
232
736
0
14 Jun 2020
On the Bottleneck of Graph Neural Networks and its Practical
  Implications
On the Bottleneck of Graph Neural Networks and its Practical Implications
Uri Alon
Eran Yahav
GNN
79
685
0
09 Jun 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu
Matthias Fey
Marinka Zitnik
Yuxiao Dong
Hongyu Ren
Bowen Liu
Michele Catasta
J. Leskovec
277
2,725
0
02 May 2020
SIGN: Scalable Inception Graph Neural Networks
SIGN: Scalable Inception Graph Neural Networks
Fabrizio Frasca
Emanuele Rossi
D. Eynard
B. Chamberlain
M. Bronstein
Federico Monti
GNN
117
396
0
23 Apr 2020
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural
  Networks
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
M. Balcilar
G. Renton
Pierre Héroux
Benoit Gaüzère
Sébastien Adam
P. Honeine
49
60
0
26 Mar 2020
Geom-GCN: Geometric Graph Convolutional Networks
Geom-GCN: Geometric Graph Convolutional Networks
Hongbin Pei
Bingzhen Wei
Kevin Chen-Chuan Chang
Yu Lei
Bo Yang
GNN
300
1,116
0
13 Feb 2020
Diffusion Improves Graph Learning
Diffusion Improves Graph Learning
Johannes Klicpera
Stefan Weißenberger
Stephan Günnemann
GNN
121
704
0
28 Oct 2019
Optimizing Generalized PageRank Methods for Seed-Expansion Community
  Detection
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection
Pan Li
Eli Chien
O. Milenkovic
61
68
0
26 May 2019
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified
  Neighborhood Mixing
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija
Bryan Perozzi
Amol Kapoor
N. Alipourfard
Kristina Lerman
Hrayr Harutyunyan
Greg Ver Steeg
Aram Galstyan
GNN
84
908
0
30 Apr 2019
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural
  Networks with Octave Convolution
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
Yunpeng Chen
Haoqi Fan
Bing Xu
Zhicheng Yan
Yannis Kalantidis
Marcus Rohrbach
Shuicheng Yan
Jiashi Feng
98
558
0
10 Apr 2019
Simplifying Graph Convolutional Networks
Simplifying Graph Convolutional Networks
Felix Wu
Tianyi Zhang
Amauri Souza
Christopher Fifty
Tao Yu
Kilian Q. Weinberger
GNN
201
3,168
0
19 Feb 2019
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Klicpera
Aleksandar Bojchevski
Stephan Günnemann
GNN
210
1,683
0
14 Oct 2018
Deeper Insights into Graph Convolutional Networks for Semi-Supervised
  Learning
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li
Zhichao Han
Xiao-Ming Wu
GNN
SSL
178
2,820
0
22 Jan 2018
Graph Attention Networks
Graph Attention Networks
Petar Velickovic
Guillem Cucurull
Arantxa Casanova
Adriana Romero
Pietro Lio
Yoshua Bengio
GNN
426
20,089
0
30 Oct 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
628
130,942
0
12 Jun 2017
Inductive Representation Learning on Large Graphs
Inductive Representation Learning on Large Graphs
William L. Hamilton
Z. Ying
J. Leskovec
452
15,179
0
07 Jun 2017
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNN
SSL
577
28,999
0
09 Sep 2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral
  Filtering
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
M. Defferrard
Xavier Bresson
P. Vandergheynst
GNN
310
7,646
0
30 Jun 2016
Spectral Networks and Locally Connected Networks on Graphs
Spectral Networks and Locally Connected Networks on Graphs
Joan Bruna
Wojciech Zaremba
Arthur Szlam
Yann LeCun
GNN
189
4,870
0
21 Dec 2013
1