Renaud Lambiotte

TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.

TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.

TL;DR: Some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks are reviewed and some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data is summarized.

TL;DR: This work presents the first empirical large-scale verification of the long-standing structural balance theory, by focusing on the specific multiplex network of friendship and enmity relations, and explores how the interdependence of different network types determines the organization of the social system.

TL;DR: It is concluded that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms, which could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.