2015
DOI: 10.1016/j.physleta.2015.04.040
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Iterative path attacks on networks

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Cited by 22 publications
(10 citation statements)
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“…Quantitative measurement of complex networks is a hot topic in network science [1], and various measurements are proposed including degree, betweenness, closeness, K-Core number and so on [2]. However, most of the measurements are for nodes and links, and only a very few are particularly for paths such as path length [36] and path attack centrality [37]. In our latest work [31], we applied information theory to measure the importance of paths, and specifically we derived the entropy of a path.…”
Section: Path Entropymentioning
confidence: 99%
“…Quantitative measurement of complex networks is a hot topic in network science [1], and various measurements are proposed including degree, betweenness, closeness, K-Core number and so on [2]. However, most of the measurements are for nodes and links, and only a very few are particularly for paths such as path length [36] and path attack centrality [37]. In our latest work [31], we applied information theory to measure the importance of paths, and specifically we derived the entropy of a path.…”
Section: Path Entropymentioning
confidence: 99%
“…These vulnerabilities include cascading effect , Buldyrev et al, 2010, node removal (Lekha and Balakrishnan, 2018), vital link identification (Liang et al, 2017), controllability of nodes , iterative path attacks (Pua et al, 2015) and seeding strategies for large scale propagation (Hinz et al, 2011). Moreover, robustness analysis and mitigation of such vulnerabilities has been investigated in Chen et al (2017), , Jiang et al (2016), Du et al (2016), Liu et al (2016), and .…”
Section: Introductionmentioning
confidence: 99%
“…Several methodologies have been proposed to measure network robustness. Approaches based on information routing (Boginski et al, 2009;Pu et al, 2012bPu et al, , 2013, structural controllability (Liu et al, 2011;Pu et al, 2012a) or in the proposal of a more destructive attack strategy in networks (Arulselvan et al, 2009;Pu et al, 2015), can be found in the literature; being the most popular those based on percolation theory (Albert et al, 2000;Cohen et al, 2000;Callaway et al, 2000), and on the size of the biggest connected component (BC) (Holme et al, 2002;Allesina and Pascual, 2009;Salathé et al, 2010;Iyer et al, 2013). Although these measures showed to be useful in many cases, they are not as sensitive as they should, to the detection of failures that do not disconnect the network or that do not modify its diameter.…”
Section: Introductionmentioning
confidence: 99%