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Author Goldreich, Oded ♦ Goldwasser, Shafi ♦ Ron, Dana
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©1998
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Approximation algorithms ♦ Computational learning theory ♦ Graph algorithms
Abstract In this paper, we consider the question of determining whether a function $\textit{f}$ has property P or is ε-far from any function with property P. A property testing algorithm is given a sample of the value of $\textit{f}$ on instances drawn according to some distribution. In some cases, it is also allowed to query $\textit{f}$ on instances of its choice. We study this question for different properties and establish some connections to problems in learning theory and approximation.In particular, we focus our attention on testing graph properties. Given access to a graph G in the form of being able to query whether an edge exists or not between a pair of vertices, we devise algorithms to test whether the underlying graph has properties such as being bipartite, $\textit{k}-Colorable,$ or having a $\textit{p}-Clique$ (clique of density $\textit{p}$ with respect to the vertex set). Our graph property testing algorithms are probabilistic and make assertions that are correct with high probability, while making a number of queries that is $\textit{independent}$ of the size of the graph. Moreover, the property testing algorithms can be used to efficiently (i.e., in time linear in the number of vertices) construct partitions of the graph that correspond to the property being tested, if it holds for the input graph.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1998-07-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 45
Issue Number 4
Page Count 98
Starting Page 653
Ending Page 750

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Source: ACM Digital Library