Graph-based approaches have been most successful in semisupervised learning. In this paper, we focus on label propagation in graph-based semisupervised learning. One essential point of label ...

Various graph-based algorithms for semi-supervised learning have been proposed in the recent literature. They rely on the idea of building a graph whose nodes are data points (labeled and unlabeled) ...

Abstract: Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label ...