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  4. Mutual Information for the Stochastic Block Model by the Adaptive Interpolation Method
 
conference paper

Mutual Information for the Stochastic Block Model by the Adaptive Interpolation Method

Barbier, Jean  
•
Chan, Chun Lam  
•
Macris, Nicolas  
January 1, 2019
2019 Ieee International Symposium On Information Theory (Isit)
IEEE International Symposium on Information Theory (ISIT)

We rigorously derive a single-letter variational expression for the mutual information of the asymmetric two-groups stochastic block model in the dense graph regime. Existing proofs in the literature are indirect, as they involve mapping the model to a rank-one matrix estimation problem whose mutual information is then determined by a combination of methods (e.g., interpolation, cavity, algorithmic, spatial coupling). In this contribution we provide a self-contained direct method using only the recently introduced adaptive interpolation method.

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