31 citations · 45 across the 4 of their papers we have counts for
4 papers
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
Yayong Li, Jie Yin, Ling Chen
Graph Neural Networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs…
Unified Robust Training for Graph NeuralNetworks against Label Noise
Yayong Li, Jie yin, Ling Chen
Graph neural networks (GNNs) have achieved state-of-the-art performance for node classification on graphs. The vast majority of existing works assume that genuine node labels are a…
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Wei Huang, Yayong Li, Weitao Du +4
Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from…
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs
Yayong Li, Jie Yin, Ling Chen
Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label…