Networks are finite metric spaces, with distances defined by the shortest paths between
nodes. However, this is not the only form of network geometry: two others are the geometry …

Networks have provided extremely successful models of data and complex systems. Yet, as
combinatorial objects, networks do not have in general intrinsic coordinates and do not …

Recent research has shown that criminal networks have complex organizational structures,
but whether this can be used to predict static and dynamic properties of criminal networks …

First-principle network models are crucial to understanding the intricate topology of real
complex networks. Although modelling efforts have been quite successful in undirected …

Reducing dimension redundancy to find simplifying patterns in high-dimensional datasets
and complex networks has become a major endeavor in many scientific fields. However …

Structural connectivity in the brain is typically studied by reducing its observation to a single
spatial resolution. However, the brain possesses a rich architecture organized over multiple …

Real networks are complex dynamical systems, evolving over time with the addition and
deletion of nodes and links. Currently, there exists no principled mathematical theory for …

Network embedding techniques aim to represent structural properties of graphs in geometric
space. Those representations are considered useful in downstream tasks such as link …

One of the pillars of the geometric approach to networks has been the development of model-
based mapping tools that embed real networks in its latent geometry. In particular, the tool …

Turing patterns, arising from the interplay between competing species of diffusive particles,
have long been an important concept for describing nonequilibrium self-organization in …