title: Community detection in networks with positive and negative links.
abstract: Detecting communities in complex networks accurately is a prime challenge, preceding further analyses of network characteristics and dynamics. Until now, community detection took into account only positively valued links, while many actual networks also feature negative links. We extend an existing Potts model to incorporate negative links as well, resulting in a method similar to the clustering of signed graphs, as dealt with in social balance theory, but more general. To illustrate our method, we applied it to a network of international alliances and disputes. Using data from 1993-2001, it turns out that the world can be divided into six power blocs similar to Huntington's civilizations, with some notable exceptions.
authors.affiliation: Department of Mathematical Engineering, Université Catholique de Louvain, Bâtiment Euler, Avenue G Lemaître, 4 B-1348 Louvain-la-neuve, Belgium. vincent.traag@uclouvain.be
authors.forename: V A
authors.initials: VA
authors.lastname: Traag
authors.forename: Jeroen
authors.initials: J
authors.lastname: Bruggeman
mesh_terms: D003198:Computer Simulation; D005246:Feedback; D008962:Models, Theoretical; D011157:Population Dynamics; D012111:Residence Characteristics; D012919:Social Behavior; D012944:Social Support