Descriptive group detection in two-mode data networks using biclustering

The search for cohesive groups inside a social network is a topic commonly known as community detection and has attracted many researchers. However, the identification of groups with competitive features using blockmodeling, biclustering and structural or regular equivalences has benefited from a less important interest within the research community. In this paper we define a generic biclustering method called BiP that computes semantically meaningful coclusters (or biclusters) from a two-mode data network. The method has the following features: (i) it allows the processing of adjacency matrices whose data type can be either binary, discrete or categorical without any need for prior data codification, and (ii) each generated block may either represent a cluster of objects with the properties they own (or not), or a juxtaposition of subgroups with distinct profiles (and sometimes purely opposed ones) for a subset of attributes.