Shiraki Yoshifumi, Kabashima Yoshiyuki
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 2268502, Japan.
Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Sep;82(3 Pt 2):036101. doi: 10.1103/PhysRevE.82.036101. Epub 2010 Sep 2.
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. 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 (random degree-dependent) attacks on sites. Our analysis indicates that the correlations among degrees affect a network's robustness against targeted attacks on sites or bonds nontrivially depending on details of network configurations.
我们基于无序系统统计力学的腔方法，开发了一种方案，用于评估随机网络中最大连通子网络（巨分量）的大小以及从网络中移除节点（位点）和/或边（键）时的渗流阈值。我们特别将该方案应用于双峰度分布的随机网络（双峰网络），在早期研究中，这种网络被提出作为抵抗位点随机故障和/或对位点的有针对性（随机度依赖）攻击的鲁棒网络。我们的分析表明，度之间的相关性会根据网络配置的细节，以非平凡的方式影响网络抵抗对位点或边的有针对性攻击的鲁棒性。
