Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …

Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …

We consider a family of problems that are concerned about making predictions for the
majority of unlabeled, graph-structured data samples based on a small proportion of labeled …

Graph-based semi-supervised learning, which further utilizes graph structure behind
samples for boosting semi-supervised learning, gains convincing results in several machine …

In the literature, most existing graph-based semi-supervised learning methods only use the
label information of observed samples in the label propagation stage, while ignoring such …

Abstract Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a
handful of labeled data to the remaining massive unlabeled data via a graph. As one of the …

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

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a
number of standard benchmark datasets for graph-based semi-supervised learning …

Graph based semi-supervised learning (SSL) methods play an increasingly important role in
practical machine learning systems. A crucial step in graph based SSL methods is the …

Predicting properties of nodes in a graph is an important problem with applications in a
variety of domains. Graph-based Semi Supervised Learning (SSL) methods aim to address …