A Simple Yet Effective SVD-GCN for Directed Graphs

A Simple Yet Effective SVD-GCN for Directed Graphs

19 May 2022

Junbin Gao

Papers citing "A Simple Yet Effective SVD-GCN for Directed Graphs"

5 / 5 papers shown

Title
Graph Convolutions Enrich the Self-Attention in Transformers! Jeongwhan Choi Hyowon Wi Jayoung Kim Yehjin Shin Kookjin Lee Nathaniel Trask Noseong Park 25 3 0 07 Dec 2023
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond Zhiqi Shao Dai Shi Andi Han Yi Guo Qianchuan Zhao Junbin Gao 13 11 0 06 Sep 2023
Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion Dai Shi Zhiqi Shao Yi Guo Qianchuan Zhao Junbin Gao 22 1 0 25 May 2023
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
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