9 citations · 12 across the 6 of their papers we have counts for
4 papers · 1 filter
Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
Junyuan Fang, Han Yang, Haixian Wen +3
Graph neural networks have been widely utilized to solve graph-related tasks because of their strong learning power in utilizing the local information of neighbors. However, recent…
Mitigating the Structural Bias in Graph Adversarial Defenses
Junyuan Fang, Huimin Liu, Han Yang +3
In recent years, graph neural networks (GNNs) have shown great potential in addressing various graph structure-related downstream tasks. However, recent studies have found that cur…
GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections
Junyuan Fang, Haixian Wen, Jiajing Wu +3
Graph neural networks (GNNs) have found successful applications in various graph-related tasks. However, recent studies have shown that many GNNs are vulnerable to adversarial atta…
MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks
Tao Huang, Yihan Zhang, Jiajing Wu +2
Graph convolutional networks (GCNs) have been employed as a kind of significant tool on many graph-based applications recently. Inspired by convolutional neural networks (CNNs), GC…