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 …

Despite degree distributions give some insights about how heterogeneous a network is, they
fail in giving a unique quantitative characterization of network heterogeneity. This is …

We study the family of network models derived by requiring the expected properties of a
graph ensemble to match a given set of measurements of a real-world network, while …

There is growing interest in multiplex networks where individual nodes take part in several
layers of networks simultaneously. This is the case, for example, in social networks where …

In this paper we generalize the concept of random networks to describe network ensembles
with nontrivial features by a statistical mechanics approach. This framework is able to …

In order to detect patterns in real networks, randomized graph ensembles that preserve only
part of the topology of an observed network are systematically used as fundamental null …

Many network systems are composed of interdependent but distinct types of interactions,
which cannot be fully understood in isolation. These different types of interactions are often …

We study the statistical properties of the sampled scale-free networks, deeply related to the
proper identification of various real-world networks. We exploit three methods of sampling …

The choice of free parameters in network models is subjective, since it depends on what
topological properties are being monitored. However, we show that the maximum likelihood …

It appeared recently that the classical random graph model used to represent real-world
complex networks does not capture their main properties. Since then, various attempts have …