How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications

14 June 2021

Papers citing "How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications"

7 / 7 papers shown

Title
MADE: Graph Backdoor Defense with Masked Unlearning Xiao Lin amd Mingjie Li Mingjie Li Yisen Wang AAML 78 1 0 03 Jan 2025
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods Donald Loveland Jiong Zhu Mark Heimann Benjamin Fish Michael T. Schaub Danai Koutra 22 6 0 10 Jul 2022
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter Yushun Dong Kaize Ding B. Jalaeian Shuiwang Ji Jundong Li 53 59 0 26 Apr 2021
Beyond Low-frequency Information in Graph Convolutional Networks Deyu Bo Xiao Wang C. Shi Huawei Shen GNN 84 445 0 04 Jan 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
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 217 1,726 0 09 Jun 2018
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 228 3,202 0 24 Nov 2016