理解复杂网络性能和脆弱性的新成果

D. Repperger
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引用次数: 0

摘要

通过图论、信息论和优化理论的共同交叉,可以得到关于复杂网络性能和脆弱性的新结果。图论提供了体系结构的基础,也为关键流变量提供了约束关系。信息论提供了流性能的度量和度量。复杂网络的优化是通过对流量变量的遗传算法来实现的。通过进行最小流量和最大流量优化,可以确定网络漏洞的敏感性矩阵。这样就可以确定最易受攻击的节点集。该程序首先应用于物流网络。讨论了该方法在通信网络和其他分布式复杂系统中的推广。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Results in Understanding Performance and Vulnerability in Complex Networks
New results can be obtained about performance and vulnerability of complex networks through the common intersection of the fields of graph theory, information theory, and optimization theory. Graph theory provides a basis of architecture and also constraint relationships for key flow variables. Information theory provides measures and metrics of flow performance. The optimization of complex networks is accomplished via genetic algorithms on the flow variables. By performing a minimum flow and maximum flow optimization, a sensitivity matrix of vulnerabilities of a network can be ascertained. Thus the most vulnerable set of nodes can be determined. This procedure is first applied to a logistics network. The generalization to communication's networks and other distributed complex systems is discussed.
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