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• W3100748105 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{i}ve 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{i}ve mean field and spectral approaches. This applies to accuracy, computational efficiency and the tendency to overfit the data." @default.
• W3100748105 created "2020-11-23" @default.
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• W3100748105 date "2012-12-20" @default.
• W3100748105 modified "2023-10-17" @default.
• W3100748105 title "Comparative study for inference of hidden classes in stochastic block models" @default.
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