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- W2912792453 abstract "We study the problem of recovering both K communities and their features from a labeled graph observation. We assume that the edges of an observed graph are generated as per the symmetric Stochastic Block Model (SBM), and that the label of each node is a noisy and partially-observed version of the corresponding community feature. We characterize the information-theoretic limit of this problem, and then propose a computationally efficient algorithm that achieves the information-theoretic limit." @default.
- W2912792453 created "2019-02-21" @default.
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- W2912792453 date "2018-10-01" @default.
- W2912792453 modified "2023-09-25" @default.
- W2912792453 title "On the Joint Recovery of Community Structure and Community Features" @default.
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- W2912792453 doi "https://doi.org/10.1109/allerton.2018.8636058" @default.
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