A Fast and Simple Unbiased Estimator for Network (Un)reliability

Karger, David

Title:

A Fast and Simple Unbiased Estimator for Network (Un)reliability

dc.contributor.author	Karger, David
dc.contributor.corporatename	Georgia Institute of Technology. Algorithms, Randomness and Complexity Center	en_US
dc.contributor.corporatename	Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory	en_US
dc.date.accessioned	2016-10-12T15:44:25Z
dc.date.available	2016-10-12T15:44:25Z
dc.date.issued	2016-09-26
dc.description	Presented on September 26, 2016 at 11:00 a.m. in the Klaus Computing Building, Room 1116E	en_US
dc.description	David Karger is a member of the Computer Science and Artificial Intelligence Laboratory in the EECS department at MIT. His primary interest is in developing tools that help individuals manage information better. This involves studying people and current tools to understand where the problems are, creating and evaluating tools that address those problems, and deploying those tools to learn how people use them and iterate the whole process. He draws on whatever fields can help: information retrieval, machine learning, databases, and algorithms, but most often human computer interaction.	en_US
dc.description	Runtime: 70:45 minutes	en_US
dc.description.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.	en_US
dc.format.extent	70:45 minutes
dc.identifier.uri	http://hdl.handle.net/1853/55915
dc.language.iso	en_US	en_US
dc.relation.ispartofseries	Algorithms and Randomness Center (ARC) Colloquium
dc.subject	Network unreliability	en_US
dc.subject	Sampling	en_US
dc.title	A Fast and Simple Unbiased Estimator for Network (Un)reliability	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	Algorithms and Randomness Center
local.contributor.corporatename	College of Computing
local.relation.ispartofseries	ARC Colloquium
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