In the last two decades, network science has blossomed and influenced various fields, such
as statistical physics, computer science, biology and sociology, from the perspective of the …

The combination of the compactness of networks, featuring small diameters, and their
complex architectures results in a variety of critical effects dramatically different from those in …

Examining important results and analytical techniques, this graduate-level textbook is a step-
by-step presentation of the structure and function of complex networks. Using a range of …

In the past 15 years, statistical physics has been successful as a framework for modelling
complex networks. On the theoretical side, this approach has unveiled a variety of physical …

Uncorrelated random scale-free networks are useful null models to check the accuracy and
the analytical solutions of dynamical processes defined on complex networks. We propose …

Random graph null models have found widespread application in diverse research
communities analyzing network datasets, including social, information, and economic …

This chapter reviews the highly interdisciplinary field of network science, a science
concerned with the study of networks, be they biological, technological, or scholarly …

This book provides a concise modern introduction to the science of complex networks. The
book is based on lectures for university students and non-specialists. The aim is to introduce …

Respondent-driven sampling is a form of link-tracing network sampling, which is widely used
to study hard-to-reach populations, often to estimate population proportions. Previous …

We analyze the degree distribution's cut-off in finite size scale-free networks. We show that
the cut-off behavior with the number of vertices N is ruled by the topological constraints …