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• W2963382956 startingPage "4339" @default.
• W2963382956 abstract "Consider a network consisting of two subnetworks (communities) connected by some external edges. Given the network topology, the community detection problem can be cast as a graph partitioning problem that aims to identify the external edges as the graph cut that separates these two subnetworks. In this paper, we consider a general model where two arbitrarily connected subnetworks are connected by random external edges. Using random matrix theory and concentration inequalities, we show that when one performs community detection via spectral clustering there exists an abrupt phase transition as a function of the random external edge connection probability. Specifically, the community detection performance transitions from almost perfect detectability to low detectability near some critical value of the random external edge connection probability. We derive upper and lower bounds on the critical value and show that the bounds are equal to each other when two subnetwork sizes are identical. Using simulated and experimental data we show how these bounds can be empirically estimated to validate the detection reliability of any discovered communities." @default.
• W2963382956 created "2019-07-30" @default.
• W2963382956 creator A5050344371 @default.
• W2963382956 creator A5077692655 @default.
• W2963382956 date "2015-08-01" @default.
• W2963382956 modified "2023-10-10" @default.
• W2963382956 title "Phase Transitions in Spectral Community Detection" @default.
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• W2963382956 doi "https://doi.org/10.1109/tsp.2015.2442958" @default.
• W2963382956 hasPublicationYear "2015" @default.
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