Affiliation:
1. Cinvestav-Tamaulipas, Mexico
2. Cinvestav-IPN, Mexico
Abstract
This Chapter studies the correlations among well-known complex network metrics and presents techniques to coarse the topology of the Internet at the Autonomous System (AS) level. We present an experimental study on the linear relationships between a rich set of complex network metrics, to methodologically select a subset of non-redundant and potentially independent metrics that explain different aspects of the topology of the Internet. Then, the selected metrics are used to evaluate graph coarsening algorithms to reduce the topology of AS networks. The presented coarsening algorithms exploit the k-core decomposition of graphs to preserve relevant complex network properties.
Reference46 articles.
1. Multilevel Algorithms for partitioning power-law graphs.;A.Abou-Rjeili;Proceedings of the 20th International Conference on Parallel and Distributed Processing,2006
2. Statistical mechanics of complex networks
3. Large scale networks fingerprinting and visualization using the k-core decomposition;J. I.Alvarez-Hamelin;Proceedings of Advances in Neural Information Processing Systems 18,2005
4. Scale-free characteristics of random networks: the topology of the world-wide web
5. Fast algorithms for determining (generalized) core groups in social networks
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