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

21 May 2024

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

9 / 9 papers shown

Title
Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspective Wentao Yu FedML 43 0 0 20 Feb 2025
DLGNet: Hyperedge Classification through Directed Line Graphs for Chemical Reactions Stefano Fiorini Giulia M. Bovolenta Stefano Coniglio Michele Ciavotta Pietro Morerio Michele Parrinello Alessio Del Bue GNN AI4CE 60 0 0 09 Oct 2024
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning Qincheng Lu Jiaqi Zhu Sitao Luan Xiao-Wen Chang 23 2 0 15 Sep 2024
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks Guangrui Yang Jianfei Li Ming Li Han Feng Ding-Xuan Zhou 23 0 0 01 Jul 2024
Heterophilous Distribution Propagation for Graph Neural Networks Zhuonan Zheng Sheng Zhou Hongjia Xu Ming Gu Yilun Xu Ao Li Yuhong Li Jingjun Gu Jiajun Bu 38 0 0 31 May 2024
Specformer: Spectral Graph Neural Networks Meet Transformers Deyu Bo Chuan Shi Lele Wang Renjie Liao 66 74 0 02 Mar 2023
How Powerful are Spectral Graph Neural Networks Xiyuan Wang Muhan Zhang 70 175 0 23 May 2022
How Framelets Enhance Graph Neural Networks Xuebin Zheng Bingxin Zhou Junbin Gao Yu Guang Wang Pietro Lió Ming Li Guido Montúfar 48 69 0 13 Feb 2021
Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei Bingzhen Wei Kevin Chen-Chuan Chang Yu Lei Bo Yang GNN 167 1,058 0 13 Feb 2020