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Robustness and assortativity for diffusion-like processes in scale-free networks

机译:无标度网络中类似扩散过程的鲁棒性和分类性

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By analysing the diffusive dynamics of epidemics and of distress in complex networks, we study the effect of the assortativity on the robustness of the networks. We first determine by spectral analysis the thresholds above which epidemics/failures can spread; we then calculate the slowest diffusional times. Our results shows that disassortative networks exhibit a higher epidemiological threshold and are therefore easier to immunize, while in assortative networks there is a longer time for intervention before epidemic/failure spreads. Moreover, we study by computer simulations the sandpile cascade model, a diffusive model of distress propagation (financial contagion). We show that, while assortative networks are more prone to the propagation of epidemic/failures, degree-targeted immunization policies increases their resilience to systemic risk.
机译:通过分析流行病和复杂网络中遇险的扩散动力学,我们研究了分类性对网络健壮性的影响。我们首先通过频谱分析确定阈值,高于阈值可以传播流行病/故障;然后,我们计算出最慢的扩散时间。我们的研究结果表明,分类网络表现出较高的流行病学阈值,因此更容易进行免疫接种,而在分类网络中,在流行/故障蔓延之前进行干预的时间更长。此外,我们通过计算机模拟研究了沙堆级联模型,这是一种遇险扩散(金融传染)的扩散模型。我们表明,尽管分类网络更易于流行/失败,但以学位为目标的免疫策略增加了其对系统性风险的适应力。

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