Characterising the robustness of complex networks

Sydney, Ali; Scoglio, Caterina; Youssef, Mina; Schumm, Phillip

doi:10.1504/ijitst.2010.037406

Cited by 36 publications

(17 citation statements)

References 25 publications

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“…Elasticity (E), the quantitative robustness metric (QNRM) and the qualitative robustness metric (QLRM) measure the robustness based on a single QoS parameter such as the throughput, the number of blocked connections or the established connections as a function of hli, respectively, [6,24]. The higher these metric values are, the more robust the network is.…”

Section: Functional Metricsmentioning

confidence: 99%

“…The random and targeted attacks affect the network performance and although networks may have similar averagecase performance under attack, they may differ significantly in their sensitivities to certain attack sequences [5]. In [6] the characteristics of network topologies that maintain a high level of throughput in spite of multiple attacks are studied.…”

Section: Introductionmentioning

confidence: 99%

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Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

2016

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Multiple failures can have catastrophic consequences on the normal operation of telecommunication networks. In this sense, guaranteeing network robustness to avoid users and services being disconnected is essential. A wide range of metrics have been proposed for measuring network robustness. In this paper the taxonomy of robustness metrics in telecommunication networks has been extended and a classification of multiple failures scenarios has been made. Moreover, a structural and centrality robustness comparison of 15 real telecommunication networks experiencing multiple failures was carried out. Through this analysis the topological properties which are common for grouping networks with similar robustness are able to be identified.

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Section: Functional Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

2016

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“…They show that after disconnecting only 10 nodes the packet-delivery ratio is reduced to 0%. Another approach, presented as an improved network attack [8,9], is to recalculate the betweenness-centrality after the removal of each node [10,11]. They show a similar impact of non-recalculating strategies but discarding sometimes only half of the equivalent nodes.…”

Section: Related Workmentioning

confidence: 99%

Miuz: measuring the impact of disconnecting a node

Bachmann¹,

Reyes²,

Silva³

et al. 2015

2015 34th International Conference of the Chilean Computer Science Society (SCCC)

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In this article we present Miuz, a robustness index for complex networks. Miuz measures the impact of disconnecting a node from the network while comparing the sizes of the remaining connected components. Strictly speaking, Miuz for a node is defined as the inverse of the size of the largest connected component divided by the sum of the sizes of the remaining ones. We tested our index in attack strategies where the nodes are disconnected in decreasing order of a specified metric. We considered Miuz and other well-known centrality measures such as betweenness, degree , and harmonic centrality. All of these metrics were compared regarding the behavior of the robust-ness (R-index) during the attacks. In an attempt to simulate the internet backbone, the attacks were performed in complex networks with power-law degree distributions (scale-free networks). Preliminary results show that attacks based on disconnecting a few number of nodes Miuz are more dangerous (decreasing the robustness) than the same attacks based on other centrality measures. We believe that Miuz, as well as other measures based on the size of the largest connected component, provides a good addition to other robustness metrics for complex networks.Comment: in 34th International Conference of the Chilean Computer Science Society (SCCC), Nov 2015, Santiago, Chile. 2015. arXiv admin note: substantial text overlap with arXiv:1601.0246

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“…The problem of finding paths through a network has been well studied in the context of graph theory [49] as well as routing and fiber network planning. The existing algorithms are based on different characteristics such as shortest paths, diverse, and disjoint paths [21], and optical restorability [22].…”

Section: Graph Theoretic Approachmentioning

confidence: 99%

Path diversification for future internet end-to-end resilience and survivability

2013

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Path Diversification is a new mechanism that can be used to select multiple paths between a given ingress and egress node pair using a quantified diversity measure to achieve maximum flow reliability. The path diversification mechanism is targeted at the end-to-end layer, but can be applied at any level for which a path discovery service is available. Path diversification also takes into account service requirements for low-latency or maximal reliability in selecting appropriate paths. Using this mechanism will allow future internetworking architectures to exploit naturally rich physical topologies to a far greater extent than is possible with shortest-path routing or equal-cost load balancing. We describe the path diversity metric and its application at various aggregation levels, and apply the path diversification process to 13 real-world network graphs as well as 4 synthetic topologies to asses the gain in flow reliability. Based on the analysis of flow reliability across a range of networks, we then extend our path diversity metric to create a compos- ite compensated total graph diversity metric that is representative of a particular topology's survivability with respect to distributed simultaneous link and node failures. We tune the accuracy of this metric having simulated the performance of each topology under a range of failure severities, and present the results. The topologies used are from nationalscale backbone networks with a variety of characteristics, which we characterize using standard graph-theoretic metrics. The end result is a compensated total graph diversity metric that accurately predicts the survivability of a given network topology.

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Characterising the robustness of complex networks

Cited by 36 publications

References 25 publications

Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

Miuz: measuring the impact of disconnecting a node

Path diversification for future internet end-to-end resilience and survivability

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