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Traffic-based network clustering

Published: 28 June 2010 Publication History

Abstract

Network clustering is traditionally approached just relying on the topology of the network, and neglecting the information on the traffic intensity between the nodes. In this paper we propose traffic-aware clustering, whereby networks are clustered on the basis of their traffic matrices. We redefine two clustering metrics for the context of traffic matrices, and perform an exploratory analysis by comparing four well known algorithms against two real-world datasets, each made of 1000 traffic matrices, respectively from Abilene and Géant networks. The Spectral Filtering algorithm appears as the best performer. However, in the Géant network dataset the two metrics provide different rankings for the algorithms under examination, and Newman's algorithm can perform marginally better under one of the two metrics.

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Cited By

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  • (2018)Mining Smart Card Data for Travellers’ Mini ActivitiesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2018.285249319:11(3676-3685)Online publication date: Nov-2018
  • (2018)The Analysis of Network Operation Situation Based on Link Traffic Matrices2018 11th International Symposium on Computational Intelligence and Design (ISCID)10.1109/ISCID.2018.00090(370-373)Online publication date: Dec-2018
  • (2012)Traffic vs topology in network clustering: Does it matter?2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC)10.1109/IWCMC.2012.6314174(36-41)Online publication date: Aug-2012
  • Show More Cited By

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cover image ACM Other conferences
IWCMC '10: Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
June 2010
1371 pages
ISBN:9781450300629
DOI:10.1145/1815396
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 June 2010

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  1. clustering
  2. graph algorithms
  3. traffic matrices

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Cited By

View all
  • (2018)Mining Smart Card Data for Travellers’ Mini ActivitiesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2018.285249319:11(3676-3685)Online publication date: Nov-2018
  • (2018)The Analysis of Network Operation Situation Based on Link Traffic Matrices2018 11th International Symposium on Computational Intelligence and Design (ISCID)10.1109/ISCID.2018.00090(370-373)Online publication date: Dec-2018
  • (2012)Traffic vs topology in network clustering: Does it matter?2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC)10.1109/IWCMC.2012.6314174(36-41)Online publication date: Aug-2012
  • (2011)An evolutionary algorithm for network clustering through traffic matrices2011 7th International Wireless Communications and Mobile Computing Conference10.1109/IWCMC.2011.5982607(1580-1584)Online publication date: Jul-2011

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