Weighted Spectral Distribution for Internet Topology Analysis: Theory and Applications

Fay, Damien; Haddadi, Hamed; Thomason, Andrew; Moore, Andrew W.; Mortier, Richard; Jamakovic, Almerima; Uhlig, Steve; Rio, Miguel

doi:10.1109/tnet.2009.2022369

Cited by 77 publications

(69 citation statements)

References 31 publications

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“…The greater number of peering links between ASes at IXPs lead to those ASes getting deeper into the core of the Internet with decreasing emphasis on connections with upper tier ASes. The authors in [36,37] have all pointed towards this evolution characteristic of the Internet and our coreness metric based result presents a theoretical confirmation of these observations. Node and edge betweenness are two measures of centrality from which further inferences can be made about the effects of IXPs peering links.…”

Section: Analysis and Discussionsupporting

confidence: 83%

Studying the Effects of Internet Exchange Points on Internet Topology

Ahmad

Guha²

2012

J Inform Tech Soft Engg

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The recent uncovering of a high number of peering links at Internet Exchange Point (IXP) locations across the world has made these exchange switches a critical component of the Internet Autonomous System (AS) level ecosystem. Studies concentrating on the internet topology evolution have surmised that numerous links hidden at these exchange points hold the key towards solving the missing links problem in studying the evolution of the AS-level topology of the Internet. In this work, we study the effect of this set of hitherto unseen peering links on the visible Internet topology. Starting with a set of measurements determining the growth of IXPs in the inter-domain routing architecture of the Internet and continuing with a more advanced graph based metric analysis of available Internet topology data, we conclude that IXP links follow power law increase characteristics while exhibiting definitive clustering characteristics. Moreover, these additional links affect the joint degree distributions of nodes with higher degrees while leaving most other types of nodes unchanged. We conclude that the currently inferred AS-level maps of the Internet demonstrate considerable variations with the incorporation of these new links and could eventually lead to a remodeling of our understanding of Internet topology evolution.

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Section: Analysis and Discussionsupporting

confidence: 83%

Studying the Effects of Internet Exchange Points on Internet Topology

Ahmad

Guha²

2012

J Inform Tech Soft Engg

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“…The normalised Laplacian spectrum of ASlevel topologies has been shown to differ significantly from that of synthetically generated topologies [21]. Recently, a weighted spectral distribution metric has been proposed and shown that synthetically generated graphs can be fine-tuned using spectral properties [22]. While previous studies utilised graph spectra to analyse logical level topologies, in this study we focus on physical networks and how they relate to each other structurally.…”

Section: Background and Related Workmentioning

confidence: 99%

Topology connectivity analysis of internet infrastructure using graph spectra

Çetinkaya

Alenazi

Rohrer

et al. 2012

2012 IV International Congress on Ultra Modern Telecommunications and Control Systems

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Abstract-Understanding and modelling the Internet has been a major research challenge in part due to the complexity of the interaction among its protocols and in part due to multilevel, multidomain topological structure. It is therefore crucial to properly analyse each structural level of the Internet to gain a better understanding, as well as to improve its resilience properties. In this paper, first we present the physical and logical topologies of two ISPs and compare these topologies with the US interstate highway topology by using graph metrics and then using the normalised Laplacian spectrum. Our results indicate that physical network topologies are closely correlated with the motorway transportation topology. Finally, we study the spectral properties of various communication networks and observe that the spectral radius of the normalised Laplacian matrix is a good indicator of graph connectivity when comparing different size and order graphs.

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“…Additionally, bipartite graphs are very relevant in the Internet, as such subgraphs correspond to nodes that have identical sets of neighbors. Studying those nodes would be of particular interest to understand the structure of the Internet as these are duplicated structures in the graph [8]. Let Third, we index the triangle structure which is a clique structure of three nodes as shown in Figure 1-(f).…”

Section: Structure 2: Complete Bipartite (K Mn )mentioning

confidence: 99%

Structural Graph Indexing for Mining Complex Networks

Kardes

Güneş

2010

2010 IEEE 30th International Conference on Distributed Computing Systems Workshops

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Abstract-Systems such as proteins, chemical compounds, and the Internet are being modeled as complex networks to identify local and global characteristics of the system. In many instances, these graphs are very large in size presenting challenges in their analysis. Hence, graph indexing techniques are developed to enhance various graph mining algorithms. In this paper, we propose a new Structural Graph Indexing (SGI) technique that does not limit the number of nodes in indexing to provide an alternative tool for graph mining algorithms. As indexing feature, we use common graph structures, namely, star, complete bipartite, triangle and clique, that frequently appear in protein, chemical compound, and Internet graphs. Note that, SGI lists all substructures matching structure formulations and other graph structures can be identified and added to the SGI.

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Weighted Spectral Distribution for Internet Topology Analysis: Theory and Applications

Cited by 77 publications

References 31 publications

Studying the Effects of Internet Exchange Points on Internet Topology

Studying the Effects of Internet Exchange Points on Internet Topology

Topology connectivity analysis of internet infrastructure using graph spectra

Structural Graph Indexing for Mining Complex Networks

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