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 …

We introduce Mercator, a reliable embedding method to map real complex networks into
their hyperbolic latent geometry. The method assumes that the structure of networks is well …

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 …

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 …

We show that the community structure of a network can be used as a coarse version of its
embedding in a hidden space with hyperbolic geometry. The finding emerges from a …

Clustering–the tendency for neighbors of nodes to be connected–quantifies the coupling of
a complex network to its latent metric space. In random geometric graphs, clustering …

Networks with underlying metric spaces attract increasing research attention in network
science, statistical physics, applied mathematics, computer science, sociology, and other …