A Regularized Stochastic Block Model for the robust community detection in complex networks
Abstract
The stochastic block model is able to generate random graphs with different types of network partitions, ranging from the traditional assortative structures to the disassortative structures. Since the stochastic block model does not specify which mixing pattern is desired, the inference algorithms discover the locally most likely nodes’ partition, regardless of its type. Here we introduce a new model constraining nodes’ internal degree ratios in the objective function to guide the inference algorithms to converge to the desired type of structure in the observed network data. We show experimentally that given the regularized model, the inference algorithms, such as Markov chain Monte Carlo, reliably and quickly find the assortative or disassortative structure as directed by the value of a single parameter. In contrast, when the sought-after assortative community structure is not strong in the observed network, the traditional inference algorithms using the degree-corrected stochastic block model tend to converge to undesired disassortative partitions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 13, 2019
- Source ID
- 10.1038/s41598-019-49580-5
Entities
People
- Boleslaw Szymanski
- Xiaoyan Lu
Organizations
- Office of Naval Research
- United States Army Research Laboratory