Comparative Study for Inference of Hidden Classes in Stochastic Block Models
arXiv:1207.2328 · doi:10.1088/1742-5468/2012/12/P12021
Abstract
Inference of hidden classes in stochastic block model is a classical problem with important applications. Most commonly used methods for this problem involve na\"ıve mean field approaches or heuristic spectral methods. Recently, belief propagation was proposed for this problem. In this contribution we perform a comparative study between the three methods on synthetically created networks. We show that belief propagation shows much better performance when compared to na\"ıve mean field and spectral approaches. This applies to accuracy, computational efficiency and the tendency to overfit the data.
8 pages, 5 figures AIGM12
References in corpus (6)
- Finding community structure in networks using the eigenvectors of matrices
- Phase transition in the detection of modules in sparse networks
- Graph spectra and the detectability of community structure in networks
- A Bayesian Approach to Network Modularity
- Community Detection as an Inference Problem
- (Un)detectable cluster structure in sparse networks