Sensibly highlighting the hidden structures of many real-world networks has attracted
growing interest and triggered a vast array of techniques on what is called nowadays …

Identifying and tracking community structures in complex networks are one of the
cornerstones of network studies, spanning multiple disciplines, from statistics to machine …

Multilayer networks continue to gain significant attention in many areas of study, particularly
due to their high utility in modeling interdependent systems such as critical infrastructures …

We propose new mathematical programming models for optimal partitioning of a signed
graph into cohesive groups. To demonstrate the approach's utility, we apply it to identify …

Graphs are ubiquitous for modeling complex systems involving structured data and
relationships. Consequently, graph representation learning, which aims to automatically …

In network science, identifying optimal partitions of a signed network into internally cohesive
and mutually divisive clusters based on generalized balance theory is computationally …

In multilayer networks, quantifying layer similarity is of great importance for many
applications. Traditional approaches to measure layer similarities rely mainly on micro-level …

Urban networks are typical multiplex networks with different forms of spatial interactions
between cities, including spatial interactions among humans, material and information. It is …

In the context of cluster analysis and graph partitioning, many external evaluation measures
have been proposed in the literature to compare two partitions of the same set. This makes …

During the past years, network dynamics has been widely investigated in various
disciplines. As a practical and convenient description for social networks, signed networks …