Centrality metrics have become a popular concept in network science and optimization.
Over the years, centrality has been used to assign importance and identify influential …

We consider the problem of performing condition-based maintenance on a set of
geographically distributed assets via a single maintenance resource that travels between …

The-vertex cut (-VC) problem belongs to the family of the critical node detection problems,
which aims to find a minimum subset of vertices whose removal decomposes a graph into at …

This study considers a well-known critical node detection problem that aims to minimize a
pairwise connectivity measure of an undirected graph via the removal of a subset of nodes …

Diffusion on networks is an important concept in network science observed in many
situations such as information spreading and rumor controlling in social networks, disease …

Biological systems, characterized by their complex interplay of symmetry and asymmetry,
operate through intricate networks of interacting molecules, weaving the elaborate tapestry …

Centrality is a powerful concept for detecting influential components of a network applicable
to various areas such as analysis of social, collaboration, and biological networks. In this …

In network analysis, node centrality is used to quantify the importance of a node to the
structure of the network. One of the most natural and widely used centrality measures is …

The spectral radius of the non-backtracking matrix for an undirected graph plays an
important role in various dynamic processes running on the graph. For example, its …

Background Identifying critical genes is important for understanding the pathogenesis of
complex diseases. Traditional studies typically comparing the change of biomecules …