Filippo Radicchi

TL;DR: This work introduces a class of benchmark graphs, that account for the heterogeneity in the distributions of node degrees and of community sizes, and uses this benchmark to test two popular methods of community detection, modularity optimization, and Potts model clustering.

TL;DR: This article proposes a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability and applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods.

TL;DR: OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.

TL;DR: It is shown that the probability that an article is cited c times has large variations between different disciplines, but all distributions are rescaled on a universal curve when the relative indicator cf = c/c0 is considered, where c0 is the average number of citations per article for the discipline.

TL;DR: The Science of Science (SciSci) as discussed by the authors provides a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales, providing insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science.