Node Feature Kernels Increase Graph Convolutional Network Robustness

4 September 2021

Papers citing "Node Feature Kernels Increase Graph Convolutional Network Robustness"

7 / 7 papers shown

Title
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks Yassine Abbahaddou Sofiane Ennadir J. Lutzeyer Michalis Vazirgiannis Henrik Bostrom AAML OOD 29 6 0 27 Apr 2024
A Simple and Yet Fairly Effective Defense for Graph Neural Networks Sofiane Ennadir Yassine Abbahaddou J. Lutzeyer Michalis Vazirgiannis Henrik Bostrom AAML 31 12 0 21 Feb 2024
Graph Neural Networks Use Graphs When They Shouldn't Maya Bechler-Speicher Ido Amos Ran Gilad-Bachrach Amir Globerson GNN AI4CE 6 14 0 08 Sep 2023
RDGSL: Dynamic Graph Representation Learning with Structure Learning Siwei Zhang Yun Xiong Yao Zhang Yiheng Sun Xiangshan Chen Yizhu Jiao Yangyong Zhu NoLa 37 11 0 05 Sep 2023
Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation Jun Zhuang M. Hasan 25 20 0 21 Aug 2022
Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei Bingzhen Wei Kevin Chen-Chuan Chang Yu Lei Bo Yang GNN 169 1,078 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 267 1,944 0 09 Jun 2018