Networks (or graphs) appear as dominant structures in diverse domains, including
sociology, biology, neuroscience and computer science. In most of the aforementioned …

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 …

This book deals with the analysis of the structure of complex networks by combining results
from graph theory, physics, and pattern recognition. The book is divided into two parts. 11 …

Coupled biological and chemical systems, neural networks, social interacting species, the
Internet and the World Wide Web, are only a few examples of systems composed by a large …

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 …

From the Internet to networks of friendship, disease transmission, and even terrorism, the
concept--and the reality--of networks has come to pervade modern society. But what exactly …

The connections in many networks are not merely binary entities, either present or not, but
have associated weights that record their strengths relative to one another. Recent studies of …

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 …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

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 …