2022/11/18   |   projects

We are looking for a highly motivated postdoctoral researcher to work at the interface between Network Geometry and Machine Learning (dimensional reduction techniques and neural networks).

Model-based Data Science

2022/11/18   |   projects

Reducing redundant information to find simplifying patterns in data sets and complex networks is a scientific challenge in many knowledge fields. Our last article published in the journal Nature Communications presents a method to infer the dimensionality of complex networks through the application of hyperbolic geometrics, which capture the complexity of relational structures of the real world in many diverse domains.

The Hyperbolic Brain

2021/06/01   |   academic

Structural brain networks are spatially embedded networks whose architecture has been shaped by physical constraints and functional needs throughout evolution. Euclidean space is typically assumed as the natural geometry of the brain. However, distances in hyperbolic space offer a more accurate interpretation of the structure of connectomes across species, including multiscale self-similarity in the human brain. Implications extend to debates, like criticality in the brain, and applications, including tools for brain simulation.

latest publications

Multiscale opinion dynamics on real networks

E. Ortiz, M. Á. Serrano
Chaos, Solitons & Fractals 165 112847 (2022)
dynamical processes  |  social systems

abstract    view    download

Detecting the ultra low dimensionality of real networks

P. Almagro, M. Boguñá, M. Á. Serrano
Nature Communications 13 6096 (2022)
theory of complex networks  |  network geometry

A geometry-induced topological phase transition in random graphs

J. van der Kolk, M. Á. Serrano, M. Boguñá
Communication Physics 5 245 (2022)
theory of complex networks  |  network geometry