title: Multiscale Community Blockmodel for Network Exploration.
abstract: Real world networks exhibit a complex set of phenomena such as underlying hierarchical organization, multiscale interaction, and varying topologies of communities. Most existing methods do not adequately capture the intrinsic interplay among such phenomena. We propose a nonparametric Multiscale Community Blockmodel (MSCB) to model the generation of hierarchies in social communities, selective membership of actors to subsets of these communities, and the resultant networks due to within- and cross-community interactions. By using the nested Chinese Restaurant Process, our model automatically infers the hierarchy structure from the data. We develop a collapsed Gibbs sampling algorithm for posterior inference, conduct extensive validation using synthetic networks, and demonstrate the utility of our model in real-world datasets such as predator-prey networks and citation networks.
authors.affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15217.
authors.forename: Qirong
authors.initials: Q
authors.lastname: Ho
authors.forename: Ankur P
authors.initials: AP
authors.lastname: Parikh
authors.forename: Eric P
authors.initials: EP
authors.lastname: Xing
keywords: Bayesian nonparametrics; Gibbs sampler; Hierarchical network analysis; Latent space model