Neuronal signalling and communication underpin virtually all aspects of brain activity and
function. Network science approaches to modelling and analysing the dynamics of …

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 …

We present a new approach for the approximate K-nearest neighbor search based on
navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The …

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 …

Understanding the mechanisms of neural communication in large-scale brain networks
remains a major goal in neuroscience. We investigated whether navigation is a …

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 …

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 …

Network models with latent geometry have been used successfully in many applications in
network science and other disciplines, yet it is usually impossible to tell if a given real …

The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here
we investigate how this intrinsic hierarchical organization of the brain shapes the …

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