The second volume of “Advancing Uncertain Combinatorics through Graphization,
Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond” …

In this paper, we conduct a comprehensive study of Trees, Forests, and Paths within the
framework of Fuzzy and Neutrosophic Graphs. Graph theory, known for its wide-ranging …

A Hyperstructure is based on the concept of a powerset, providing a framework to model
relationships among elements within a set. Extending this idea, a SuperHyperStructure …

Protein function prediction is a complex multiclass multilabel classification problem,
characterized by multiple issues such as the incompleteness of the available annotations …

The fused lasso, also known as (anisotropic) total variation denoising, is widely used for
piecewise constant signal estimation with respect to a given undirected graph. The fused …

We study the problem of learning a binary classifier on the vertices of a graph. In particular,
we consider classifiers given by\emph {monophonic halfspaces}, partitions of the vertices …

The first step in graph-based semi-supervised classification is to construct a graph from input
data. While the k-nearest neighbor graphs have been the de facto standard method of graph …

Gene function prediction is a complex multilabel classification problem with several
distinctive features: the hierarchical relationships between functional classes, the presence …

This paper studies the online node classification problem under a transductive learning
setting. Current methods either invert a graph kernel matrix with $\mathcal {O}(n^ 3) …

We investigate the problem of active learning on a given tree whose nodes are assigned
binary labels in an adversarial way. Inspired by recent results by Guillory and Bilmes, we …