Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks—the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain—and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.
The objective of this article is to demonstrate the feasibility of on-demand creation of cloud-based elastic mobile core networks, along with their lifecycle management. For this purpose the article describes the key elements to realize the architectural vision of EPC as a Service, an implementation option of the Evolved Packet Core, as specified by 3GPP, which can be deployed in cloud environments. To meet several challenging requirements associated with the implementation of EPC over a cloud infrastructure and providing it "as a Service," this article presents a number of different options, each with different characteristics, advantages, and disadvantages. A thorough analysis comparing the different implementation options is also presented
Abstract-We study the algebraic connectivity in relation to the graph's robustness to node and link failures. Graph's robustness is quantified with the node and the link connectivity, two topological metrics that give the number of nodes and links that have to be removed in order to disconnect a graph. The algebraic connectivity, i.e. the second smallest eigenvalue of the Laplacian matrix, is a spectral property of a graph, which is an important parameter in the analysis of various robustness-related problems. In this paper we study the relationship between the proposed metrics in three well-known complex network models: the random graph of Erdős-Rényi, the smallworld graph of Watts-Strogatz and the scale-free graph of Barabási-Albert. From [11] it is known that the algebraic connectivity is a lower bound on both the node and the link connectivity. Through extensive simulations with the three complex network models, we show that the algebraic connectivity is not trivially connected to graph's robustness to node and link failures. Furthermore, we show that the tightness of this lower bound is very dependent on the considered complex network model.
Abstract-Comparing graphs to determine the level of underlying structural similarity between them is a widely encountered problem in computer science. It is particularly relevant to the study of Internet topologies, such as the generation of synthetic topologies to represent the Internet's AS topology. We derive a new metric that enables exactly such a structural comparison, the weighted spectral distribution. We then apply this metric to three aspects of the study of the Internet's AS topology. (i) we use it to quantify the effect of changing the mixing properties of a simple synthetic network generator. (ii) we use this quantitative understanding to examine the evolution of the Internet's AS topology over approximately 7 years, finding that the distinction between the Internet core and periphery has blurred over time.(iii) we use the metric to derive optimal parameterizations of several widely used AS topology generators with respect to a large-scale measurement of the real AS topology.
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