3 papers
cs.LG2025
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…
cs.LG2024
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…
cs.LG2024
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…