Multiscale Community Blockmodel for Network Exploration
By Qirong Ho, Ankur P. Parikh, Le Song, Eric P. Xing. 2011
46 citations recorded by the source corpus.
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.
Subfields: Molecular Biology, Statistical and Nonlinear Physics
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In Molecular Biology
In Statistical and Nonlinear Physics
- Prem Gopalan
- David M. Blei
- David Mimno
- Sean Gerrish
- Michael J. Freedman
- Jake M. Hofman
- Hao Wang
- Dit-Yan Yeung
Citations in the corpus
- Scalable Inference of Overlapping Communities (2012, background): “The MMSB can capture complex community structure and has been adapted in several ways [11, 12]; however, its applications have been limited because its corresponding inference algorithms have not scaled to large networks [2].”
- Deep Generative Models for Relational Data with Side Information (2017, unknown): “Stochastic blockmodels have also been extended for inferring nested communities using nested Chinese Restraurant Process (Ho et al., 2012).”