title: Module identification in bipartite and directed networks.
abstract: Modularity is one of the most prominent properties of real-world complex networks. Here, we address the issue of module identification in two important classes of networks: bipartite networks and directed unipartite networks. Nodes in bipartite networks are divided into two nonoverlapping sets, and the links must have one end node from each set. Directed unipartite networks only have one type of node, but links have an origin and an end. We show that directed unipartite networks can be conveniently represented as bipartite networks for module identification purposes. We report on an approach especially suited for module detection in bipartite networks, and we define a set of random networks that enable us to validate the approach.
authors.affiliation: Northwestern Institute on Complex Systems (NICO) and Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA.
authors.forename: Roger
authors.initials: R
authors.lastname: Guimerà
authors.forename: Marta
authors.initials: M
authors.lastname: Sales-Pardo
authors.forename: Luís A Nunes
authors.initials: LA
authors.lastname: Amaral
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012944:Social Support