基于随机距离的复杂网络测度变异性分析
Variability Analysis of Complex Networks Measures based on Stochastic Distances
摘要：
复杂网络可以模拟不同类型系统的结构和动力学。它已被证明，他们的特点是一套措施。在这项工作中，我们评估的复杂网络措施面临的扰动的变化，并为此目的，我们施加控制扰动和量化其效果。我们分析了理论模型（随机，小世界和无标度）和真实网络（协作网络和代谢网络）以及最短路径长度，顶点度，局部聚类系数和介数中心性度量。在这样的分析中，我们建议使用三个随机量词：Kullback-Leibler分歧和詹森-香农和海林格距离。这些措施的灵敏度进行了分析，相对于以下扰动：边缘添加，边缘删除，边缘重新布线和节点删除，所有这些应用在不同的强度。结果表明，评估措施的影响，这些扰动。此外，进行假设检验以验证度分布的行为，以确定导致破坏此属性的扰动强度。
英文摘要：
Complex networks can model the structure and dynamics of different types of systems. It has been shown that they are characterized by a set of measures. In this work, we evaluate the variability of complex networks measures face to perturbations and, for this purpose, we impose controlled perturbations and quantify their effect. We analyze theoretical models (random, small-world and scale-free) and real networks (a collaboration network and a metabolic networks) along with the shortest path length, vertex degree, local cluster coefficient and betweenness centrality measures. In such analysis, we propose the use of three stochastic quantifiers: the Kullback-Leibler divergence and the Jensen-Shannon and Hellinger distances. The sensitivity of these measures was analyzed with respect to the following perturbations: edge addition, edge removal, edge rewiring and node removal, all of them applied at different intensities. The results reveal that the evaluated measures are influenced by these perturbations. Additionally, hypotheses tests were performed to verify the behavior of the degree distribution to identify the intensity of the perturbations that leads to break this property.
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