How Powerful are Spectral Graph Neural Networks

23 May 2022

Papers citing "How Powerful are Spectral Graph Neural Networks"

10 / 10 papers shown

Title
Learning Laplacian Positional Encodings for Heterophilous Graphs Michael Ito Jiong Zhu Dexiong Chen Danai Koutra Jenna Wiens 71 1 0 29 Apr 2025
Humans as a Calibration Pattern: Dynamic 3D Scene Reconstruction from Unsynchronized and Uncalibrated Videos Changwoon Choi Jeongjun Kim Geonho Cha Minkwan Kim Dongyoon Wee Young Min Kim 3DH 39 0 0 26 Dec 2024
THeGCN: Temporal Heterophilic Graph Convolutional Network Yuchen Yan Yuzhong Chen Huiyuan Chen Xiaoting Li Zhe Xu Zhichen Zeng Lihui Liu Zhining Liu Hanghang Tong 72 0 0 21 Dec 2024
Sparse Decomposition of Graph Neural Networks Yaochen Hu Mai Zeng Ge Zhang P. Rumiantsev Liheng Ma Yingxue Zhang Mark Coates 22 0 0 25 Oct 2024
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction Junjie Xu Artem Moskalev Tommaso Mansi Mangal Prakash Rui Liao AI4CE 23 1 0 15 Oct 2024
Bundle Neural Networks for message diffusion on graphs Jacob Bamberger Federico Barbero Xiaowen Dong Michael M. Bronstein 23 1 0 24 May 2024
Contextualized Messages Boost Graph Representations Brian Godwin Lim Galvin Brice Lim Renzo Roel Tan Kazushi Ikeda AI4CE 33 1 0 19 Mar 2024
Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias Zhihao Shi Jie Wang Fanghua Lu Hanzhu Chen Defu Lian Zheng Wang Jieping Ye Feng Wu AI4CE 9 6 0 26 Sep 2023
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
Multi-scale Attributed Node Embedding Benedek Rozemberczki Carl Allen Rik Sarkar GNN 139 828 0 28 Sep 2019