I am a PhD student at the Complex Multilayer Networks Lab at the Fondazione Bruno Kessler, in Trento.
Here, I work on network geometry, a field at the intersection between complex networks (graph and spectral graph theory), geometry (differential, discrete, information, and spectral geometry), and dynamical systems (stochastic processes). The first review (Boguñá et al. 2021) on this topic is very recent, it was published only in January 2021!
Boguñá, Marián, Ivan Bonamassa, Manlio De Domenico, Shlomo Havlin, Dmitri Krioukov, and M. Ángeles Serrano,. 2021. “Network Geometry” 3 (2): 114–35. doi: 10.1038/s42254-020-00264-4.
Download my resumé.
PhD student in Mathematics, 2021
University of Trento and CoMuNe Lab, FBK, Trento, Italy
MSc in Mathematics for Life Sciences, 2019
University of Trento, Italy
Master (first level) in Data Science, 2015-2016
Bologna Business School, Bologna, Italy
BSc in Mathematics, 2014
University of Trento, Italy
90%
90%
80%
R packages
Centrality descriptors are widely used to rank nodes according to specific concept(s) of importance. Despite the large number of centrality measures available nowadays, it is still poorly understood how to identify the node which can be considered as the ‘centre’ of a complex network. In fact, this problem corresponds to finding the median of a complex network. The median is a non-parametric—or better, distribution-free—and robust estimator of the location parameter of a probability distribution. In this work, we present the statistical and most natural generalization of the concept of median to the realm of complex networks, discussing its advantages for defining the centre of the system and percentiles around that centre. To this aim, we introduce a new statistical data depth and we apply it to networks embedded in a geometric space induced by different metrics. The application of our framework to empirical networks allows us to identify central nodes which are socially or biologically relevant.