This paper aims to propose a framework for manifold regularization (MR) based distributed
semi-supervised learning (DSSL) using single layer feed-forward neural network (SLFNN) …

This book is a general introduction to the statistical analysis of networks, and can serve both
as a research monograph and as a textbook. Numerous fundamental tools and concepts …

Many existing federated learning (FL) algorithms are designed for supervised learning tasks,
assuming that the local data owned by the clients are well labeled. However, in many …

This paper aims to propose a distributed semi-supervised learning (D-SSL) algorithm to
solve D-SSL problems, where training samples are often extremely large-scale and located …

In this paper, we propose two manifold regularization (MR) based distributed semi-
supervised learning (DSSL) algorithms using the random vector functional link (RVFL) …

Graph filters play an important role in graph signal processing, in which the data is analyzed
with respect to the underlying network (graph) structure. As an extension to classical signal …

In this paper, we propose a distributed semi-supervised learning (DSSL) algorithm based on
the extreme learning machine (ELM) algorithm over communication network using the event …

Semi-supervised learning (SSL) has been applied to many practical applications over the
past few years. Recently, distributed graph-based semi-supervised learning (DGSSL) has …

The processing of signals defined on graphs has been of interest for many years, and finds
applications in a diverse set of fields such as sensor networks, social and economic …

Nowadays, graph-based semi-supervised learning (GB-SSL) is a fast-growing area of
classifying nodes in a graph with an extremely low number of labelled nodes. However, the …