30 citations · 59 across the 8 of their papers we have counts for
Showing 2021Show all
3 papers · 1 filter
cs.LG2021★ 5 cited
Adaptive Multi-layer Contrastive Graph Neural Networks
Shuhao Shi, Pengfei Xie, Xu Luo +4
We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN), a self-supervised learning framework for Graph Neural Network, which learns feature representations of…
cs.LG2021★ 3 cited
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout
Pengfei Xie, Linyuan Wang, Ruoxi Qin +4
Deep neural networks(DNNs) is vulnerable to be attacked by adversarial examples. Black-box attack is the most threatening attack. At present, black-box attack methods mainly adopt…
cs.LG2021★ 30 cited
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
S. Shi, Kai Qiao, Shuai Yang +3
The Graph Neural Network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dat…