Effect of degree-preserving, assortative rewiring on OSPF router configuration

Noldus, Rogier; Mieghem, Piet Van

doi:10.1109/itc.2013.6662963

Cited by 3 publications

(6 citation statements)

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“…Winterbach et al [55] study a greedy degreepreserving rewiring approach. Van Mieghem et al [49] and Noldus and Van Mieghem [36] apply a targeted approach for selecting links to be rewired in order to affect the assortativity, without affecting the degree sequence.…”

Section: Litvak and Van Der Hofstadmentioning

confidence: 99%

“…Noldus and Van Mieghem [36] describe a method for assortative rewiring by ordering nodes according to their degree. The first link of the node pair is formed by selecting the node with highest degree and selecting the link connecting that node with another node having the lowest degree of all nodes connected to this first node.…”

Section: Influencing Assortativity Through Network Topology Changesmentioning

confidence: 99%

“…For DPR, link pairs can be found that will increase the assortativity of the network (Van Mieghem et al [49], Noldus and Van Mieghem [36]). What is not yet explored is how to deterministically find the link pair that will provide the largest absolute increase or decrease in assortativity.…”

Section: Influencing Assortativity Through Network Topology Changesmentioning

confidence: 99%

See 2 more Smart Citations

Assortativity in complex networks

2015

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We survey the concept of assortativity, starting from its original definition by Newman in 2002. Degree assortativity is the most commonly used form of assortativity. Degree assortativity is extensively used in network science. Since degree assortativity alone is not sufficient as a graph analysis tool, assortativity is usually combined with other graph metrics. Much of the research on assortativity considers undirected, non-weighted networks. The research on assortativity needs to be extended to encompass also directed links and weighted links. In addition, the relation between assortativity and line graphs, complementary graphs and graph spectra needs further work, to incorporate directed graphs and weighted links. The present survey paper aims to summarize the work in this area and provides a new scope of research.

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Section: Litvak and Van Der Hofstadmentioning

confidence: 99%

Section: Influencing Assortativity Through Network Topology Changesmentioning

confidence: 99%

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Assortativity in complex networks

2015

Self Cite

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“…To evaluate the significance of the clustering coefficient and the mean shortest path length, the network was compared with randomly rewired networks, which preserved the number of nodes and degree distributions of the original (Evans, ; Kiss & Green, ; Maslov & Sneppen, ; Noldus & Van Mieghem, ). One thousand rewired networks were developed using the igraph software package (Csardi & Nepusz, ), where in such rewired networks the probability of rewiring was set at one, resulting in all arcs in the LSMN being swapped.…”

Section: Methodsmentioning

confidence: 99%

Network structure and risk‐based surveillance algorithms for live shrimp movements in Thailand

Saleetid

Green

2019

Transbound Emerg Dis

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Live shrimp movements pose a potential route for site‐to‐site transmission of acute hepatopancreatic necrosis disease (AHPND) and other shrimp diseases. We present the first application of network theory to study shrimp epizootiology, providing quantitative information about the live shrimp movement network of Thailand (LSMN), and supporting practical and policy implementations of disease surveillance and control measures. We examined the LSMN over a 13‐month period from March 2013 to March 2014, with data obtained from the Thailand Department of Fisheries. The LSMN had a mixture of characteristics both limiting and facilitating disease spread. Importantly, the LSMN exhibited power‐law distributions of in and out degrees with exponents of 2.87 and 2.17, respectively. This characteristic indicates that the LSMN behaves like a scale‐free network and suggests that an effective strategy to control disease spread in the Thai shrimp farming sector can be achieved by removing a small number of targeted inter‐site connections (arcs between nodes). Specifically, a disease‐control algorithm based on betweenness centrality (defined as the number of shortest paths between node pairs that traverse a given arc) is proposed here to prioritize targets for disease surveillance and control measures.

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“…In this method, the assortativity was changed without changing the degree distribution because the individual rewirings did not change the degree distribution. Although this rewiring method did not necessarily achieve the overall maximum or minimum assortativity, this method has been widely used in previous researches relevant to assortativity due to its low computational cost (Xulvi-Brunet and Sokolov 2004; Van Mieghem et al 2010; Noldus and Van Mieghem 2013), and it was also discussed that rewiring method can well approximate optimal solutions (Winterbach et al 2012). …”

Section: Methodsmentioning

confidence: 99%

Robustness and efficiency in interconnected networks with changes in network assortativity

et al. 2017

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In this study, the effect of assortativity on the robustness and efficiency of interconnected networks was investigated. This involved constructing a network that possessed the desired degree of assortativity. Additionally, an interconnected network was constructed wherein the assortativity between component networks possessed the desired value. With respect to single networks, the results indicated that a decrease in assortativity provided low hop length, high information diffusion efficiency, and distribution of communication load on edges. The study also revealed that excessive assortativity led to poor network performance. In the study, the assortativity between networks was defined and the following results were demonstrated: assortative connections between networks lowered the average hop length and enhanced information diffusion efficiency, whereas disassortative connections between networks distributed the communication loads of internetwork links and enhanced robustness. Furthermore, it is necessary to carefully adjust assortativity based on the node degree distribution of networks. Finally, the application of the results to the design of robust and efficient information networks was discussed.

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Effect of degree-preserving, assortative rewiring on OSPF router configuration

Cited by 3 publications

References 10 publications

Assortativity in complex networks

Assortativity in complex networks

Network structure and risk‐based surveillance algorithms for live shrimp movements in Thailand

Robustness and efficiency in interconnected networks with changes in network assortativity

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