Original paper
Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks
Abstract
We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength...
Paper Details
Title
Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks
Published Date
Jul 13, 2016
Journal
Volume
6
Issue
3
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History