The algorithmic aspects of the regularity lemma
Journal of Algorithms
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Clustering and sharing incentives in BitTorrent systems
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Epidemic live streaming: optimal performance trade-offs
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Szemerédi's regularity lemma and its applications to pairwise clustering and segmentation
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Approximate Hypergraph Partitioning and Applications
SIAM Journal on Computing
Szemerédi's regularity lemma for matrices and sparse graphs
Combinatorics, Probability and Computing
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In this work we made a preliminary clustering analysis of an experimental peer-to-peer system, tested in a small scale experiments within PlanetLab. The application was an Internet-TV like streaming system based on Chord architecture. Our clustering is inspired by the Szemerédi's Regularity Lemma (SzRL). Such approach was already demonstrated in biology and appeared to be a powerful tool. Szemerédi's result suggests that the nodes of a large enough graph can be partitioned in few clusters in such a way that link distribution between most of the pairs look like random. Our main goal is to study what can this type of clustering tell us about p2p systems using our experimental system as source of data. We searched clusterings of Szemerédi-type by using max likelihood as guidance. Our graph is directed and weighted. The link direction indicates a client-server relation and the value is the proportion of all chunks obtained from such a link during the whole experiment. We think that the preliminary results are interesting. Most of the cluster pairs have very distinguished patterns of link distribution, indicating that such a novel approach has potential in classifying peers effectively. The values of weights between clusters and their distribution show some apparent patterns. We end up with 9 cluster pairs. Contributions: practical implementations of streaming system by V. P. and analysis by H. R.