Spectral Analysis of Internet Topologies

Abstract— We perform spectral analysis of the Internet topology at the AS level, by adapting the standard spectral filtering method of examining the eigenvectors corresponding to the largest eigenvalues of matrices related to the adjacency matrix of the topology. We observe that the method suggests clusters of ASes with natural semantic proximity, such as geography or business interests. We examine how these clustering properties vary in the core and in the edge of the network, as well as across geographic areas, over time, and between real and synthetic data. We observe that these clustering properties may be suggestive of traffic patterns and thus have direct impact on the link stress of the network. Finally, we use the weights of the eigenvector corresponding to the first eigenvalue to obtain an alternative hierarchical ranking of the ASes.

• Type: Conference
• Year: 2003
• Where: INFOCOM
• Authors: Christos Gkantsidis, Milena Mihail, Ellen W. Zegura
• Keywords: Communications, INFOCOM 2003, Method Suggests Clusters, Natural Semantic Proximity, Spectral filtering Method