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Algorithmic detectability threshold of the stochastic block model

Tatsuro Kawamoto
Phys. Rev. E 97, 032301 – Published 2 March 2018

Abstract

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation–maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

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  • Received 30 October 2017

DOI:https://doi.org/10.1103/PhysRevE.97.032301

©2018 American Physical Society

Physics Subject Headings (PhySH)

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Authors & Affiliations

Tatsuro Kawamoto

  • Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo, Japan

Article Text

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References

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Issue

Vol. 97, Iss. 3 — March 2018

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