1 citations · 1 across the 5 of their papers we have counts for
5 papers
Normalize Then Propagate: Efficient Homophilous Regularization for Few-shot Semi-Supervised Node Classification
Baoming Zhang, MingCai Chen, Jianqing Song +3
Graph Neural Networks (GNNs) have demonstrated remarkable ability in semi-supervised node classification. However, most existing GNNs rely heavily on a large amount of labeled data…
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck
Shuangjie Li, Jiangqing Song, Baoming Zhang +3
Graph neural networks (GNNs) are prominent for their effectiveness in processing graph data for semi-supervised node classification tasks. Most works of GNNs assume that the observ…
Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
Shuangjie Li, Baoming Zhang, Jianqing Song +3
Graph Neural Networks (GNNs) have gained considerable prominence in semi-supervised learning tasks in processing graph-structured data, primarily owing to their message-passing mec…
Similarity-Navigated Conformal Prediction for Graph Neural Networks
Jianqing Song, Jianguo Huang, Wenyu Jiang +3
Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks. However, these results lack reliable uncertainty estimates. Conformal predicti…
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
Mingcai Chen, Yuntao Du, Wei Tang +4
In real-world applications, perfect labels are rarely available, making it challenging to develop robust machine learning algorithms that can handle noisy labels. Recent methods ha…