Etienne Lefebvre's research while affiliated with Imperial College London and other places

Publications (4)

Article
Full-text available
We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called m...
Article
Full-text available
L'identification de sous-groupes denses dans les grands réseaux d'interactions est un problème complexe auquel on se retrouve confronté dès lors que l'on essaye de décrire précisément la structure d'un grand graphe. Le calcul de ces groupes, ou communautés, revient à chercher une partition de l'ensemble des sommets qui maximise une fonction de qual...

Citations

... We have applied MDMC to brain and social networks, as well as synthesized networks planted with pervasive communities, and have demonstrated that MDMC can properly detect pervasive communities from these networks. Modularity maximization [39][40][41][42][43][44] and map equation [33][34][35][36] are the prevailing methods for community detection that exploit random walk, but they cannot help discover the pervasive structure of communities 1 . Moreover, they rely on greedy search such as the Louvain method 56 . ...
... One of broadly used traditional methods is based on the Louvain clustering method [259]. It comes from the community detection family to extract community structure from large networks. ...
... The procedure of detecting the existence of subgroups or sub-communities within a network. This is the sets of actors with a higher degree of interconnections among them ( Blondel et al., 2008;Newman, 2006). In this way it is possible to identify the different subgroups (called modules) that make up the general or complete network. ...