The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

Several research studies have shown that complex networks modeling real-world
phenomena are characterized by striking properties:(i) they are organized according to …

Many community detection algorithms have been developed to uncover the mesoscopic
properties of complex networks. However how good an algorithm is, in terms of accuracy …

Stochastic blockmodels have been proposed as a tool for detecting community structure in
networks as well as for generating synthetic networks for use as benchmarks. Most …

The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

We consider the problem of detecting communities or modules in networks, groups of
vertices with a higher-than-average density of edges connecting them. Previous work …

Community detection and analysis is an important methodology for understanding the
organization of various real-world networks and has applications in problems as diverse as …

In this survey we overview the definitions and methods for graph clustering, that is, finding
sets of “related” vertices in graphs. We review the many definitions for what is a cluster in a …

Each complex network (or class of networks) presents specific topological features which
characterize its connectivity and highly influence the dynamics of processes executed on the …

Modularity, presumably shaped by evolutionary constraints, underlies the functionality of
most complex networks ranged from social to biological networks. However, it remains …