I am a research fellow at the Department of Mathematics of the University of Trento.
Here, I work on information geometry and statistics with prof. Claudio Agostinelli.
Download my resumé.
PhD cum laude in Mathematics, 2018-2021
University of Trento and CoMuNe Lab, FBK, Trento, Italy
MSc in Mathematics for Life Sciences, 2016-2018
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.