2 citations · 5 across the 5 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2023
Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module
Jingbo Zhou, Yixuan Du, Ruqiong Zhang +7
Graph Neural Networks (GNNs), a type of neural network that can learn from graph-structured data through neighborhood information aggregation, have shown superior performance in va…
cs.LG2023★ 2 cited
Adaptive Depth Graph Attention Networks
Jingbo Zhou, Yixuan Du, Ruqiong Zhang +1
As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely…
cs.LG2022★ 2 cited
Robust Training of Graph Neural Networks via Noise Governance
Siyi Qian, Haochao Ying, Renjun Hu +4
Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. However, the robustness of GNNs in the presence of label noise remains a largely under-exp…