Article

 

Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks Público Deposited

https://scholar.colorado.edu/concern/articles/2227mq30j
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 of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.
Creator
Date Issued
  • 2016-07-13
Academic Affiliation
Journal Title
Journal Issue/Number
  • 3
Journal Volume
  • 6
File Extent
  • 031005-031005-9
Subject
Última modificación
  • 2019-12-06
Resource Type
Declaración de derechos
DOI
ISSN
  • 2160-3308
Language

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