Multiscale community blockmodel for network exploration

Real world networks exhibit a complex set of phenomena such as underlying hier-archical organization, multiscale interaction, and varying topologies of communities. Most existing methods do not adequately capture the intrinsic interplay among such 1 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 valida-tion using synthetic networks, and demonstrate the utility of our model in real-world datasets such as predator-prey networks and citation networks