article

Quasirandom rumor spreading: An experimental analysis

Authors:

Benjamin Doerr,

Tobias Friedrich,

Marvin Künnemann,

Thomas SauerwaldAuthors Info & Claims

Journal of Experimental Algorithmics (JEA), Volume 16

Article No.: 3.3, Pages 3.1 - 3.13

https://doi.org/10.1145/1963190.2025379

Published: 16 November 2008 Publication History

Abstract

We empirically analyze two versions of the well-known “randomized rumor spreading” protocol to disseminate a piece of information in networks. In the classical model, in each round, each informed node informs a random neighbor. In the recently proposed quasirandom variant, each node has a (cyclic) list of its neighbors. Once informed, it starts at a random position of the list, but from then on informs its neighbors in the order of the list.

While for sparse random graphs a better performance of the quasirandom model could be proven, all other results show that, independent of the structure of the lists, the same asymptotic performance guarantees hold as for the classical model.

In this work, we compare the two models experimentally. Not only does this show that the quasirandom model generally is faster, but it also shows that the runtime is more concentrated around the mean. This is surprising given that much fewer random bits are used in the quasirandom process.

These advantages are also observed in a lossy communication model, where each transmission does not reach its target with a certain probability, and in an asynchronous model, where nodes send at random times drawn from an exponential distribution. We also show that typically the particular structure of the lists has little influence on the efficiency.

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Cited By

Wang ZZhao HNie H(2019)Bibliometric Analysis of Rumor Propagation Research Through Web of Science from 1989 to 2019Journal of Statistical Physics10.1007/s10955-019-02440-y178:2(532-551)Online publication date: 20-Nov-2019
https://doi.org/10.1007/s10955-019-02440-y
Shiraga TYamauchi YKijima SYamashita M(2018)Deterministic Random Walks for Rapidly Mixing ChainsSIAM Journal on Discrete Mathematics10.1137/16M108766732:3(2180-2193)Online publication date: 30-Aug-2018
https://doi.org/10.1137/16M1087667
Avin CElsässer R(2018)Breaking the $$\log n$$logn barrier on rumor spreadingDistributed Computing10.1007/s00446-017-0312-431:6(503-513)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s00446-017-0312-4
Show More Cited By

Index Terms

Quasirandom rumor spreading: An experimental analysis
1. Theory of computation
  1. Randomness, geometry and discrete structures

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Published In

cover image ACM Journal of Experimental Algorithmics

ACM Journal of Experimental Algorithmics Volume 16, Issue

2011

411 pages

ISSN:1084-6654

EISSN:1084-6654

DOI:10.1145/1963190

Issue’s Table of Contents

Copyright © 2008 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2008

Published in JEA Volume 16

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Cited By

Wang ZZhao HNie H(2019)Bibliometric Analysis of Rumor Propagation Research Through Web of Science from 1989 to 2019Journal of Statistical Physics10.1007/s10955-019-02440-y178:2(532-551)Online publication date: 20-Nov-2019
https://doi.org/10.1007/s10955-019-02440-y
Shiraga TYamauchi YKijima SYamashita M(2018)Deterministic Random Walks for Rapidly Mixing ChainsSIAM Journal on Discrete Mathematics10.1137/16M108766732:3(2180-2193)Online publication date: 30-Aug-2018
https://doi.org/10.1137/16M1087667
Avin CElsässer R(2018)Breaking the $$\log n$$logn barrier on rumor spreadingDistributed Computing10.1007/s00446-017-0312-431:6(503-513)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s00446-017-0312-4
Giakkoupis GNazari YWoelfel PGiakkoupis G(2016)How Asynchrony Affects Rumor Spreading TimeProceedings of the 2016 ACM Symposium on Principles of Distributed Computing10.1145/2933057.2933117(185-194)Online publication date: 25-Jul-2016
https://dl.acm.org/doi/10.1145/2933057.2933117
Berenbrink PElsässer RFriedetzky T(2016)Efficient randomised broadcasting in random regular networks with applications in peer-to-peer systemsDistributed Computing10.1007/s00446-016-0264-029:5(317-339)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s00446-016-0264-0
Doerr BWahlström M(2016)How to Generate Randomized Roundings with Dependencies and How to Derandomize ThemAlgorithm Engineering10.1007/978-3-319-49487-6_5(159-184)Online publication date: 11-Nov-2016
https://doi.org/10.1007/978-3-319-49487-6_5
Haeupler BPandurangan GPeleg DRajaraman RSun Z(2016)Discovery Through GossipRandom Structures & Algorithms10.1002/rsa.2062148:3(565-587)Online publication date: 6-Jan-2016
https://doi.org/10.1002/rsa.20621
Kijima SKoga KMakino K(2015)Deterministic random walks on finite graphsRandom Structures & Algorithms10.1002/rsa.2053346:4(739-761)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1002/rsa.20533
Doerr BFriedrich TSauerwald T(2014)Quasirandom Rumor SpreadingACM Transactions on Algorithms10.1145/265018511:2(1-35)Online publication date: 30-Oct-2014
https://dl.acm.org/doi/10.1145/2650185
Doerr BHuber ALevavi A(2013)Strong robustness of randomized rumor spreading protocolsDiscrete Applied Mathematics10.1016/j.dam.2012.10.014161:6(778-793)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1016/j.dam.2012.10.014
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