104 citations · 118 across the 5 of their papers we have counts for
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cs.LG2021
CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph
Xugang Wu, Huijun Wu, Xu Zhou +1
Graph neural networks exhibit remarkable performance in graph data analysis. However, the robustness of GNN models remains a challenge. As a result, they are not reliable enough to…
cs.LG2019★ 104 cited
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy +3
Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep lea…
cs.LG2017★ 5 cited
Interpreting Shared Deep Learning Models via Explicable Boundary Trees
Huijun Wu, Chen Wang, Jie Yin +2
Despite outperforming the human in many tasks, deep neural network models are also criticized for the lack of transparency and interpretability in decision making. The opaqueness r…