Santo Fortunato

Santo Fortunato is a professor is the School of Informatics and Computing at IU Bloomington. 

Fortunato received his PhD in Theoretical Physics in 2000 at the Department of Physics of the University of Bielefeld, Germany, working on lattice gauge theories, percolation and phenomenology of heavy-ion collisions. He switched to complexity science in 2004, and from 2005 till 2007 he has been a postdoctoral researcher at the School of Informatics and Computing of Indiana University, working in the group of Alessandro Vespignani. From 2007 till 2011 Santo has been at ISI Foundation in Turin, Italy, first as research scientist then as a scientific leader. In 2011 he joined the the School of Science of Aalto University, Finland, where he was full professor in the Department of Computer Science before returning to IU. 

Picture of Santo Fortunato
Professor
School of Informatics and Computing

Recent papers

  • Do you want to know how to recover communities using both structure and metadata? In Network strucure, metadata, and the prediction of missing nodes and annotations (Physical Review X 6, 031038, 2016) we introduce a method, based on a posteriori stochastic block modeling, that allows to combine both inputs, checking if and how they are compatible and predicting structure from metadata and vice versa.
  • My user guide to community detection in networks is online (arXiv:1608.00163): check it out.
  • In the paper Detection of gene communities in multi-networks reveals cancer drivers (Scientific Reports 5, 17386, 2015) we show that, by combining communities found in different types of genetic networks, one can identify candidate driving genes in cancer more easily than from the communities of individual networks
  • In the paper Quantifying randomness in real networks (Nature Communications 6, 8627, 2015) we show that many features of real networks are the same if we randomize them by keeping their degree distributions, degree correlations and clustering. We conclude that many statistical properties of networks are consequences of such local observables