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https://doi.org/10.1109/sfcs.1...
Article . 1986 . Peer-reviewed
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SIAM Journal on Computing
Article . 1991 . Peer-reviewed
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An optimal randomized parallel algorithm for finding connected components in a graph

Authors: Hillel Gazit;

An optimal randomized parallel algorithm for finding connected components in a graph

Abstract

Summary: A parallel randomized algorithm for finding the connected components of an undirected graph is presented. The algorithm has an expected running time of \(T=O(\log(n))\) with \(P=O((m+n)/\log(n))\) processors, where \(m\) is the number of edges and \(n\) is the number of vertices. The algorithm is optimal in the sense that the product \(P\cdot T\) is a linear function of the input size. The algorithm requires \(O(m+n)\) space, which is the input size, so it is optimal in space as well.

Related Organizations
Keywords

Connectivity, connected components, Graph theory (including graph drawing) in computer science, Analysis of algorithms and problem complexity, Modes of computation (nondeterministic, parallel, interactive, probabilistic, etc.), parallel randomized algorithm, Paths and cycles, CRCW

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
101
Top 10%
Top 10%
Top 10%
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