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小第13行：
第13行： − 复杂网络的鲁棒性研究对许多领域都非常重要。在生态学中，鲁棒性是生态系统的重要属性，可以使人们深入了解诸如物种灭绝等干扰因素的反应。对于生物学家而言，网络鲁棒性可以帮助研究疾病和突变，以及如何从某些突变中恢复过来。对于经济学，网络鲁棒性原则可以帮助理解银行系统的稳定性和风险。同时在工程中，网络鲁棒性可以帮助评估基础建设网络（如互联网或电网）的恢复能力。+ 第25行：
第25行： − 复杂网络的鲁棒性主要是关注因删除节点或链接后的网络响应情况。可以将这个过程的数学模型视为逆渗透过程。渗流理论模拟了将小卵石以概率''p''随机放置在n维晶格上的过程，并以临界概率来预测突然形成单个簇群的过程。在渗流理论中，该簇群被称为渗流簇。这种现象可以通过许多参数来量化，例如平均聚类s。它表示所有有限簇的平均大小，并由以下方程式给出。+
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The study of robustness in complex networks is important for many fields. In ecology, robustness is an important attribute of ecosystems, and can give insight into the reaction to disturbances such as the extinction of species. For biologists, network robustness can help the study of diseases and mutations, and how to recover from some mutations. In economics, network robustness principles can help understanding of the stability and risks of banking systems. And in engineering, network robustness can help to evaluate the resilience of infrastructure networks such as the Internet or power grids.
The study of robustness in complex networks is important for many fields. In ecology, robustness is an important attribute of ecosystems, and can give insight into the reaction to disturbances such as the extinction of species. For biologists, network robustness can help the study of diseases and mutations, and how to recover from some mutations. In economics, network robustness principles can help understanding of the stability and risks of banking systems. And in engineering, network robustness can help to evaluate the resilience of infrastructure networks such as the Internet or power grids.
复杂网络的鲁棒性研究对许多领域都非常重要。在生态学中，鲁棒性是生态系统的重要属性，可以使人们深入了解对诸如物种灭绝等干扰因素的反应。对于生物学家而言，网络鲁棒性可以帮助研究疾病和突变，以及如何从某些突变中恢复过来。对于经济学，网络鲁棒性原则可以帮助理解银行系统的稳定性和风险。同时在工程中，网络鲁棒性可以帮助评估基础建设网络（如互联网或电网）的恢复能力。
The focus of robustness in complex networks is the response of the network to the removal of nodes or links. The mathematical model of such a process can be thought of as an inverse percolation process. Percolation theory models the process of randomly placing pebbles on an n-dimensional lattice with probability p, and predicts the sudden formation of a single large cluster at a critical probability <math>p_c</math>. In percolation theory this cluster is named the percolating cluster. This phenomenon is quantified in percolation theory by a number of quantities, for example the average cluster size <math>\langle s \rangle</math>. This quantity represents the average size of all finite clusters and is given by the following equation.
The focus of robustness in complex networks is the response of the network to the removal of nodes or links. The mathematical model of such a process can be thought of as an inverse percolation process. Percolation theory models the process of randomly placing pebbles on an n-dimensional lattice with probability p, and predicts the sudden formation of a single large cluster at a critical probability <math>p_c</math>. In percolation theory this cluster is named the percolating cluster. This phenomenon is quantified in percolation theory by a number of quantities, for example the average cluster size <math>\langle s \rangle</math>. This quantity represents the average size of all finite clusters and is given by the following equation.
复杂网络的鲁棒性主要是关注移除节点或链接后的网络响应情况。可以将这个过程的数学模型视为逆渗透过程。渗流理论模拟了将小卵石以概率''p''随机放置在n维晶格上的过程，并以临界概率来预测突然形成单个簇群的过程。在渗流理论中，该簇群被称为渗流簇。这种现象可以通过许多参数来量化，例如平均簇大小s。它表示所有有限簇的平均大小，并由以下方程式给出: