2016
DOI: 10.1007/978-3-662-53426-7_4
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Fast Distributed Algorithms for Testing Graph Properties

Abstract: We initiate a thorough study of distributed property testing -producing algorithms for the approximation problems of property testing in the CONGEST model. In particular, for the so-called dense graph testing model we emulate sequential tests for nearly all graph properties having 1-sided tests, while in the general and sparse models we obtain faster tests for trianglefreeness, cycle-freeness and bipartiteness, respectively. In addition, we show a logarithmic lower bound for testing bipartiteness and cycle-fre… Show more

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Cited by 15 publications
(2 citation statements)
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“…Property testing algorithms for triangle-freeness were given by Censor-Hillel et al [8]; for H-freeness for graphs on 4 nodes by Fraigniaud et al [19] and Even et al [15]; and for H-freeness for most graphs on 5 nodes by Fischer et al [16] Moreover, property testing algorithms for tree and cycle freeness-using techniques of fixed-parameter algorithmics flavour-have been discovered recently [14][15][16]19].…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Property testing algorithms for triangle-freeness were given by Censor-Hillel et al [8]; for H-freeness for graphs on 4 nodes by Fraigniaud et al [19] and Even et al [15]; and for H-freeness for most graphs on 5 nodes by Fischer et al [16] Moreover, property testing algorithms for tree and cycle freeness-using techniques of fixed-parameter algorithmics flavour-have been discovered recently [14][15][16]19].…”
Section: Related Workmentioning
confidence: 99%
“…In this work, we study this problem in the CONGEST model of distributed computation. So far, most work has either focused on (a) lower bounds for subgraph detection [12], (b) randomised upper bounds [16,20], or (c) property testing of H-freeness [8,16,18]. By contrast, we focus on deterministic upper bounds, showing that simple techniques-indeed, often techniques well-known in non-distributed algorithmics-result in essentially optimal algorithms.…”
Section: Introductionmentioning
confidence: 99%