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The Rationality of Four Metrics of Network Robustness: A Viewpoint of Robust Growth of Generalized Meshes

机译:网络鲁棒性的四个度量的合理性:广义网格的鲁棒增长的观点

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摘要

There are quite a number of different metrics of network robustness. This paper addresses the rationality of four metrics of network robustness (the algebraic connectivity, the effective resistance, the average edge betweenness, and the efficiency) by investigating the robust growth of generalized meshes (GMs). First, a heuristic growth algorithm (the Proximity-Growth algorithm) is proposed. The resulting proximity-optimal GMs are intuitively robust and hence are adopted as the benchmark. Then, a generalized mesh (GM) is grown up by stepwise optimizing a given measure of network robustness. The following findings are presented: (1) The algebraic connectivity-optimal GMs deviate quickly from the proximity-optimal GMs, yielding a number of less robust GMs. This hints that the rationality of the algebraic connectivity as a measure of network robustness is still in doubt. (2) The effective resistace-optimal GMs and the average edge betweenness-optimal GMs are in line with the proximity-optimal GMs. This partly justifies the two quantities as metrics of network robustness. (3) The efficiency-optimal GMs deviate gradually from the proximity-optimal GMs, yielding some less robust GMs. This suggests the limited utility of the efficiency as a measure of network robustness.
机译:网络健壮性有很多不同的指标。本文通过研究广义网格(GM)的稳健增长,探讨了网络稳健性的四个指标(代数连接性,有效阻力,平均边际间隔和效率)的合理性。首先,提出了一种启发式增长算法(Proximity-Growth algorithm)。由此产生的接近最佳GM具有直观的鲁棒性,因此被用作基准。然后,通过逐步优化网络鲁棒性的给定度量来长大通用网格(GM)。提出以下发现:(1)代数连通性最优GMs与邻近最优GMs迅速偏离,产生了许多不那么健壮的GMs。这暗示了代数连通性作为衡量网络鲁棒性的合理性仍存在疑问。 (2)有效电阻最优GMs和平均边缘间最优GMs与接近最优GMs一致。这部分证明了这两个数量是网络鲁棒性的指标。 (3)效率最优的GM逐渐偏离接近最优的GM,从而产生了一些不那么健壮的GM。这表明效率作为网络鲁棒性度量的用途有限。

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