title: Generating attributed networks with communities.
abstract: In many modern applications data is represented in the form of nodes and their relationships, forming an information network. When nodes are described with a set of attributes we have an attributed network. Nodes and their relationships tend to naturally form into communities or clusters, and discovering these communities is paramount to many applications. Evaluating algorithms or comparing algorithms for automatic discovery of communities requires networks with known structures. Synthetic generators of networks have been proposed for this task but most solely focus on connectivity and their properties and overlook attribute values and the network properties vis-à-vis these attributes. In this paper, we propose a new generator for attributed networks with community structure that dependably follows the properties of real world networks.
authors.affiliation: Hubert Curien Laboratory, University of Lyon, Saint-Étienne, France.
authors.forename: Christine
authors.initials: C
authors.lastname: Largeron
authors.affiliation: Hubert Curien Laboratory, University of Lyon, Saint-Étienne, France.
authors.forename: Pierre-Nicolas
authors.initials: PN
authors.lastname: Mougel
authors.affiliation: Department of Computer Science, University of Alberta, Edmonton, Canada.
authors.forename: Reihaneh
authors.initials: R
authors.lastname: Rabbany
authors.affiliation: Department of Computer Science, University of Alberta, Edmonton, Canada.
authors.forename: Osmar R
authors.initials: OR
authors.lastname: Zaïane
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D008962:Models, Theoretical