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• W2071738667 abstract "We developed a scheme for evaluating the size of the largest connected subnetwork (giant component) in random networks and the percolation threshold when sites (nodes) and/or bonds (edges) are removed from the networks based on the cavity method of statistical mechanics of disordered systems. An advantage of our scheme is the capability of handling targeted attacks on sites/bonds in the presence of degree correlations beyond naive analyses on random failures (crashes) in networks of no degree correlations. We apply our scheme particularly to random networks of bimodal degree distribution (two-peak networks), which have been proposed in earlier studies as robust networks against random failures of site and/or targeted attacks on sites, and show that the correlations among degrees affect a network's robustness against targeted attacks on sites or bonds non-trivially depending on details of network configurations." @default.
• W2071738667 created "2016-06-24" @default.
• W2071738667 creator A5002600110 @default.
• W2071738667 creator A5076272438 @default.
• W2071738667 date "2010-09-02" @default.
• W2071738667 modified "2023-10-18" @default.
• W2071738667 title "Cavity analysis on the robustness of random networks against targeted attacks: Influences of degree-degree correlations" @default.
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• W2071738667 doi "https://doi.org/10.1103/physreve.82.036101" @default.
• W2071738667 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21230133" @default.
• W2071738667 hasPublicationYear "2010" @default.
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