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 …

In the past years, network theory has successfully characterized the interaction among the
constituents of a variety of complex systems, ranging from biological to technological, and …

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 …

A network representation is useful for describing the structure of a large variety of complex
systems. However, most real and engineered systems have multiple subsystems and layers …

The success of new scientific areas can be assessed by their potential in contributing to new
theoretical approaches and in applications to real-world problems. Complex networks have …

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 …

Simplicial complexes are generalized network structures able to encode interactions
occurring between more than two nodes. Simplicial complexes describe a large variety of …

The quantification of the complexity of networks is, today, a fundamental problem in the
physics of complex systems. A possible roadmap to solve the problem is via extending key …

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 …

Similarity of nodes is a basic structure quantification in complex networks. Lots of methods in
research on complex networks are based on nodes' similarity such as node's classification …