Comparative Study for Inference of Hidden Classes in Stochastic Block Models

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.

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