2023/02/03   |   projects

We are looking for a highly motivated postdoctoral researcher to work at the interface between Network Science and Machine Learning.

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.

recent publications

Random graphs and real networks with weak geometric coupling

J. van der Kolk, M. Á. Serrano, M. Boguñá
arXiv:2312.07416 (2023)

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Optimal navigability of weighted human brain connectomes in physical space

L. Barjuan, J. Soriano, M. Ángeles Serrano
arXiv:2311.10669 (2023)
systems biology

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Feature-enriched network geometry explains graph-structured data

Roya Aliakbarisani, M. Ángeles Serrano, Marián Boguñá
arXiv preprint arXiv:2307.14198 (2023)

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