Santo's Website

Director of the Indiana University Network Science Institute (IUNI)

Member of the Center for Complex Networks and Systems Research (CNetS)

Professor at the Luddy School of Informatics, Computing, and Engineering (SICE)

Indiana University, Bloomington, USA

News

• Since January 2020 I am a member of the editorial board of Quantitative Science Studies, the official open access journal of the International Society for Scientometrics and Informetrics
• I am Chair and lecturer of the first Summer Institute in Network Science and its Applications (SINSA 2020), a two-weeks school with eight modules taught by top scholars in network science, covering various aspects of the discipline. SINSA 2020 will be hosted at Northeastern University in Boston in June 22-July 3 2020. Send your best students to SINSA 2020!
• I am PI of AccelNet-MultiNet (October 2019-September 2024), a $1.9M NSF funded project that will establish the first international exchange program in network science. See our press release
• I am Chair of Networks 2021, possibly the largest conference on networks in history, which will be held in Washington DC on July 6-11, 2021
• I am a co-PI in a $5M grant from the Minerva Research Initiative (July 2019-August 2024), aiming at uncovering the basic laws ruling science dynamics. Here is a press release
• Since April 2018, I am the director of the Indiana University Network Science Institute (IUNI)

Recent papers

• The paper on the DREAM Challenge on Disease Module Identification in genetic networks is finally out in Nature Methods
• If you want to know why Nobel Laureates are almost the same as us, take a look at our paper in Nature Reviews Physics!
• Check out new great maps of the rat's brain in our PNAS paper!
• Our review article on the science of science has been published in Science magazine (Science 359, eaao0185, 2018). Big team effort with an amazing group of scientists
• The paper Multiresolution consensus clustering in networks (Scientific Reports 8, 3259, 2018) introduces a technique to combine partitions of clustering methods at various resolution levels. It has the advantage of not choosing specific resolution levels via arbitrary criteria, and it is capable to detect hierarchical community structure