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"

7 / 7 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 30 4 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 33 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 34 1 0 25 May 2023
A Fractional Graph Laplacian Approach to Oversmoothing Sohir Maskey Raffaele Paolino Aras Bacho Gitta Kutyniok 27 29 0 22 May 2023
Generalized energy and gradient flow via graph framelets Andi Han Dai Shi Zhiqi Shao Junbin Gao 75 13 0 08 Oct 2022
How Framelets Enhance Graph Neural Networks Xuebin Zheng Bingxin Zhou Junbin Gao Yu Guang Wang Pietro Lió Ming Li Guido Montúfar 59 69 0 13 Feb 2021
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 259 3,239 0 24 Nov 2016