ACM Computing Surveys (CSUR)
Clustering Algorithms
A cluster algorithm for graphs
A cluster algorithm for graphs
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
A Distributed Approach to Node Clustering in Decentralized Peer-to-Peer Networks
IEEE Transactions on Parallel and Distributed Systems
Providing public intradomain traffic matrices to the research community
ACM SIGCOMM Computer Communication Review
IEEE Transactions on Knowledge and Data Engineering
Traffic matrix reloaded: impact of routing changes
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
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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.