The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

Despite substantial recent progress, our understanding of the principles and mechanisms
underlying complex brain function and cognition remains incomplete. Network neuroscience …

Complex networks have been successfully used to describe the spread of diseases in
populations of interacting individuals. Conversely, pairwise interactions are often not …

It is nearly 20 years since the concept of a small-world network was first quantitatively
defined, by a combination of high clustering and short path length; and about 10 years since …

Network theory provides an intuitively appealing framework for studying relationships
among interconnected brain mechanisms and their relevance to behaviour. As the space of …

Various systems in physics, biology, social sciences and engineering have been
successfully modeled as networks of coupled dynamical systems, where the links describe …

With the impressive growth of available data and the flexibility of network modelling, the
problem of devising effective quantitative methods for the comparison of networks arises …

Topological data analysis (TDA) can broadly be described as a collection of data analysis
methods that find structure in data. These methods include clustering, manifold estimation …

Persistent homology (PH) is a method used in topological data analysis (TDA) to study
qualitative features of data that persist across multiple scales. It is robust to perturbations of …

Encoding brain regions and their connections as a network of nodes and edges captures
many of the possible paths along which information can be transmitted as humans process …