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• W2975352502 abstract "The information-theoretic limits of community detection have been studied extensively for network models with high levels of symmetry or homogeneity. The contribution of this paper is to study a broader class of network models that allow for variability in the sizes and behaviors of the different communities, and thus better reflect the behaviors observed in real-world networks. Our results show that the ability to detect communities can be described succinctly in terms of a matrix of effective signal-to-noise ratios that provides a geometrical representation of the relationships between the different communities. This characterization follows from a matrix version of the I-MMSE relationship and generalizes the concept of an effective scalar signal-to-noise ratio introduced in previous work. We provide explicit formulas for the asymptotic per-node mutual information and upper bounds on the minimum mean-squared error. The theoretical results are supported by numerical simulations." @default.
• W2975352502 created "2019-10-03" @default.
• W2975352502 creator A5056559023 @default.
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• W2975352502 date "2019-07-01" @default.
• W2975352502 modified "2023-09-23" @default.
• W2975352502 title "The Geometry of Community Detection via the MMSE Matrix" @default.
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• W2975352502 doi "https://doi.org/10.1109/isit.2019.8849594" @default.
• W2975352502 hasPublicationYear "2019" @default.
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