A Multiscale Community Blockmodel for Network Exploration

Multiscale Community Blockmodel for Network Exploration

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 m...

A state-space mixed membership blockmodel for dynamic network tomography

In a dynamic social or biological environment, the interactions between the underlying actors can undergo large and systematic changes. The latent roles or membership of the actors as determined by these dynamic links will also exhibit rich temporal phenomena, assuming a distinct role at one point while leaning more towards a second role at an another point. To capture this dynamic mixed member...

Network histograms and universality of blockmodel approximation

In this paper we introduce the network histogram, a statistical summary of network interactions to be used as a tool for exploratory data analysis. A network histogram is obtained by fitting a stochastic blockmodel to a single observation of a network dataset. Blocks of edges play the role of histogram bins and community sizes that of histogram bandwidths or bin sizes. Just as standard histogra...

Bayesian approach to multilayer stochastic blockmodel and network changepoint detection

A Network for Multiscale Image Segmentation

Deterling edges of ol>jecfs in their images is a ba.roblc»> in computa.lional vision. The scale-space technique inii'Oduced by Witkin [I 1} provides means of using local and global r·m.<cmi»g i» lora.ling edges. This approach has a major drawback: il is diflicu/1 to obtain accumtely the /ocati011S of /he 'semantically meaningful' edges. We have refined the deflnilion of scale-space,'""' i...