Title:
A Fast and Simple Unbiased Estimator for Network (Un)reliability
A Fast and Simple Unbiased Estimator for Network (Un)reliability
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Author(s)
Karger, David
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Abstract
The following procedure yields an unbiased estimator for the disconnection probability of an n-vertex graph with minimum cut c if every edge fails independently with probability p: (i) contract every edge independently with probability 1-n^{-2/c}, then (ii) recursively compute the disconnection probability of the resulting tiny graph if each edge fails with probability n^{2/c}p. We give a short, simple, self-contained proof that this estimator can be computed in linear time and has relative variance O(n^2). Combining these two facts with a relatively standard sparsification argument yields an O(n^3\log n)-time algorithm for estimating the (un)reliability of a network. We also show how the technique can be used to create unbiased samples of disconnected networks.
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Date Issued
2016-09-26
Extent
70:45 minutes
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Moving Image
Resource Subtype
Lecture