Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical
and practical perspectives. They are one of the most fundamental types of stochastic …

The renormalization group is the cornerstone of the modern theory of universality and phase
transitions and it is a powerful tool to scrutinize symmetries and organizational scales in …

Coarse‐grained (CG) modeling is an invaluable tool for the study of polymers and other soft
matter systems due to the span of spatiotemporal scales that typify their physics 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 …

Assessing the navigability of interconnected networks (transporting information, people, or
goods) under eventual random failures is of utmost importance to design and protect critical …

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 …

A large number of complex systems find a natural abstraction in the form of weighted
networks whose nodes represent the elements of the system and the weighted edges …

The statistical mechanical approach to complex networks is the dominant paradigm in
describing natural and societal complex systems. The study of network properties, and their …

We analyze the connectivity structure of weighted brain networks extracted from
spontaneous magnetoencephalographic signals of healthy subjects and epileptic patients …