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 …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Abstract Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the
intersection of graph clustering and semi-supervised learning (SSL), offering innovative …

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …

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 …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

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 …

Graph-based semi-supervised learning (GSSL) is an important paradigm among semi-
supervised learning approaches and includes the two processes of graph construction and …

We propose a theoretical framework to analyze semi-supervised classification under the low
density separation assumption in a high-dimensional regime. In particular, we introduce …

Most of graph construction techniques assume a transductive setting in which the whole
data collection is available at construction time. Addressing graph construction for inductive …