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 …

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 …

Represented as graphs, real networks are intricate combinations of order and disorder.
Fixing some of the structural properties of network models to their values observed in real …

Entropic measures of complexity are able to quantify the information encoded in complex
network structures. Several entropic measures have been proposed in this respect. Here we …

Network theory provides novel concepts that promise an improved characterization of
interacting dynamical systems. Within this framework, evolving networks can be considered …

Complex networks datasets often come with the problem of missing information: interactions
data that have not been measured or discovered, may be affected by errors, or are simply …

Generating random networks efficiently and accurately is an important challenge for
practical applications, and an interesting question for theoretical study. This book presents …

We study the dynamics of the generalized Lotka-Volterra model with a network structure.
Performing a high connectivity expansion for graphs, we write down a mean-field dynamical …

We present a linear stability analysis of stationary states (or fixed points) in large dynamical
systems defined on random, directed graphs with a prescribed distribution of indegrees and …

Many of the structural characteristics of a network depend on the connectivity with and within
the hubs. These dependencies can be related to the degree of a node and the number of …