Community detection and Role extraction in networks
Intervenant(s) : Arnaud Browet (Université catholique de Louvain)
Network clustering, also known as graph partitioning, has focused the attention of many researchers during the last decade due to the increasing number of potential applications of network science. However, the amount of collected data makes their analysis a real challenge and dedicated algorithms must be developed in order to comprehend them. In this talk, I will first introduce recent developments on community detection and present a fast and highly parallelizable algorithm which outperforms existing methods in term of computational time. Yet, a community structure is not always representative of the actual distribution of the nodes in a network. For example, bipartite or cycle graphs often do not contain communities and are in fact more closely represented by what is known as a role structure. In a second part of the talk, I will present a pairwise node similarity measure which allows to extract those role structures and demonstrate its performance on synthetic and real graphs.
francois.queyroi (at) nulllip6.fr