News & past events

We are hiring

M. B. applications 01/02/2023

Postdoctoral position in Network Science - Machine Learning

The Mapping Complexity Lab of Prof. M. Ángeles Serrano and Prof. M. Boguñá is opening a call to hire a postdoctoral researcher in the Department of Condensed Matter Physics at the University of Barcelona.


A PhD in physics, computer science, computer/electronic engineering, mathematics, or other related disciplines.
Interest in interdisciplinary research, curiosity about AI and networks, high motivation to learn, an open-minded and collaborative spirit.
Excellent software development skills.
Excellent communication skills, and proficient in the English language, both written and spoken.


The successful applicant will work with Prof. M. Ángeles Serrano and Prof. Marián Boguñá at the interface between Network Science and Machine Learning. The goal is to merge the best of the two worlds to produce a new generation of models and methods for the classification and prediction of complex networks. We offer a 2-year position (1+1) with a competitive salary.

Application process:

Interested applicants are requested to submit a Curriculum Vitae including relevant publications and the name and contact details of 2 referees.

We promote diversity and equal opportunities, minorities in science are encouraged to apply.
Queries about this position should be sent to or

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New paper in Nature Communications

M. B. publications 16/10/2022

Reducing dimension redundancy to find simplifying patterns in high-dimensional datasets and complex networks has become a major endeavor in many scientific fields. However, detecting the dimensionality of their latent  space is challenging but necessary to generate efficient embeddings to be used in a multitude of downstream tasks. Here, we propose a method to infer the dimensionality of networks without the need for any a priori spatial embedding. Due to the ability of hyperbolic geometry to capture the complex connectivity of real networks, we detect ultra low dimensionality far below values reported using other approaches. We applied our method to real networksfrom different domains and found unexpected regularities, including: tissue-specific biomolecular networks being extremely low dimensional; brain connectomes being close to the three dimensions of their anatomical embedding; and social networks and the Internet requiring slightly higher dimensionality. Beyond paving the way towards an ultra efficient dimensional reduction, our findings help address fundamental issues that hinge on dimensionality, such as universality in critical behavior. Read the paper here

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Cambridge Elements

M. B. publications 04/12/2021

A short introduction to the exciting field of network geometry.

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New paper in PNAS

M. B. publications 21/06/2021

Scaling up real networks by geometric branching growth

Branching processes underpin the complex evolution of manyreal systems. However, network models typically describe net-work growth in terms of a sequential addition of nodes. Here,we measured the evolution of real networks—journal cita-tions and international trade—over a 100-y period and foundthat they grow in a self-similar way that preserves their struc-tural features over time. This observation can be explained bya geometric branching growth model that generates a mul-tiscale unfolding of the network by using a combination ofbranching growth and a hidden metric space approach. Ourmodel enables multiple practical applications, including thedetection of optimal network size for maximal response to anexternal influence and a finite-size scaling analysis of criticalbehavior. Read the paper.

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Review on Network Geometry

M. B. publications 21/06/2021

Review on Network Geometry published in Nature Reviews Physics

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 of latent spaces underlying many networks and the effective geometry induced by dynamical processes in networks. These three approaches to network geometry are intimately related, and all three of them have been found to be exceptionally efficient in discovering fractality, scale invariance, self-similarity and other forms of fundamental symmetries in networks. Network geometry is also of great use in a variety of practical applications, from understanding how the brain works to routing in the Internet. We review the most important theoretical and practical developments dealing with these approaches to network geometry and offer perspectives on future research directions and challenges in this frontier in the study of complexity. Read the paper.

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The physics of fun

M. Boguñá publications 05/11/2019

Engaging in playful activities, such as playing a musical instrument, learning a language, or per- forming sports, is a fundamental aspect of human life. We present a quantitative empirical analysis of the engagement dynamics into playful activities. We do so by analyzing the behavior of millions of players of casual video games and discover a scaling law governing the engagement dynamics. This power-law behavior is indicative of a multiplicative (i.e., happy- get-happier) mechanism of engagement characterized by a set of critical exponents. Read more

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Postdoctoral position available (closed)

M. B. applications 19/06/2018

Postdoctoral Position, Mapping Big Data Systems

Applications are invited for a Postdoctoral fellowship at the University of Barcelona to work with Prof. M. Ángeles Serrano and Prof. Marián Boguñá at the Department of Condensed Matter Physics and UB Institute of Complex Systems (UBICS). The position is funded by a "Fundación BBVA" grant.

This is a postdoctoral position with a salary of €29,700 per year (before taxes) for one year renewable until june 2020. There will be additional support for conference travels.

The topic:

The discovery and understanding of the hidden geometry of complex networks have become fundamental problems within Network Science giving place to the field of Network Geometry. The research will focus on mixing of machine learning and statistical techniques for an efficient embedding of very large networks in hidden metric spaces to produce meaningful maps. We will create an online platform to host an atlas of network maps, and where scientists and interested people can produce theirs.

Essential Skills:

- A PhD in Physics, Computer Science, Computer/Electronic Engineering, or other relevant discipline is expected.
- Expertise in complex networks
- Excellent software development skills
- Excellent communication skills, verbal and written (English)

Informal queries about this position should be sent to

Application process:

Interested applicants are requested to submit

- a Curriculum Vitae including relevant publications and the name and contact details of 2 referees

- a one page cover letter explaining your interest in this specific position

Submissions should be sent by email with subject “MBDS Application” to

Interviews will be carried out as soon as suitable candidates are identified.

To successful candidate is expected to start in September  2018, or before (if possible).

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