1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Perseus: Leveraging Common Data Patterns with Curriculum Learning for More Robust Graph Neural Networks
Kaiwen Xia, Huijun Wu, Duanyu Li +3
Graph Neural Networks (GNNs) excel at handling graph data but remain vulnerable to adversarial attacks. Existing defense methods typically rely on assumptions like graph sparsity a…
cs.LG2024★ 1 cited
Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs
Duanyu Li, Huijun Wu, Min Xie +3
Graph neural network (GNN) models play a pivotal role in numerous tasks involving graph-related data analysis. Despite their efficacy, similar to other deep learning models, GNNs a…