• Bernoulli
  • Volume 19, Number 2 (2013), 676-719.

Cluster detection in networks using percolation

Ery Arias-Castro and Geoffrey R. Grimmett

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We consider the task of detecting a salient cluster in a sensor network, that is, an undirected graph with a random variable attached to each node. Motivated by recent research in environmental statistics and the drive to compete with the reigning scan statistic, we explore alternatives based on the percolative properties of the network. The first method is based on the size of the largest connected component after removing the nodes in the network with a value below a given threshold. The second method is the upper level set scan test introduced by Patil and Taillie [Statist. Sci. 18 (2003) 457–465]. We establish the performance of these methods in an asymptotic decision- theoretic framework in which the network size increases. These tests have two advantages over the more conventional scan statistic: they do not require previous information about cluster shape, and they are computationally more feasible. We make abundant use of percolation theory to derive our theoretical results, and complement our theory with some numerical experiments.

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Bernoulli, Volume 19, Number 2 (2013), 676-719.

First available in Project Euclid: 13 March 2013

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cluster detection connected components largest open cluster within a box multiple hypothesis testing percolation scan statistic surveillance upper level set scan statistic


Arias-Castro, Ery; Grimmett, Geoffrey R. Cluster detection in networks using percolation. Bernoulli 19 (2013), no. 2, 676--719. doi:10.3150/11-BEJ412.

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