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Complex Network Reliability Analysis based on Entropy Theory

机译:基于熵理论的复杂网络可靠性分析

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Network reliability is an essential issue of complex networks; the reliability of complex networks plays an important role in the performance in the research process. At the same time, the number of connected nodes in a complex network is a main measure of the complex network. Due to the randomness of complex networks, we define one new degree sequence and the entropy of the complex network, and we then study the entropy of the network as a new measure for the network reliability. The features of entropy are studied in complex networks, and entropy is analyzed in two representative complex network models, the random network model and scale-free network model. The degree distributions functions in the random network model and scale-free network model have significantly different characteristics, the Poisson distribution and Power-law distribution. Furthermore, we study the entropy features under two nodes fault models, random failures and deliberate attacks. We discuss the entropy of the random network model and scale-free network model in two fault modes with the fault intensity gradually increasing from 0 to 1.0. Then, we study the relation between the average degree distribution and the entropy of the network when the fault intensity is 0.3. The results show that the entropy of the network is reasonable to measure the network reliability similar to the number of connected nodes in the network. The purpose of the research is to provide a new way to study network reliability.
机译:网络可靠性是复杂网络的重要问题;复杂网络的可靠性在研究过程中的性能中起着重要作用。同时,复杂网络中的连接节点的数量是复杂网络的主要测量。由于复杂网络的随机性,我们定义了一个新的度序列和复杂网络的熵,然后我们将网络的熵作为网络可靠性的新措施研究。在复杂的网络中研究了熵的特征,并在两个代表性的复杂网络模型中分析了熵,随机网络模型和无尺度网络模型分析。随机网络模型的程度分布在无尺度网络模型中具有显着不同的特征,泊松分布和幂律分布。此外,我们研究两个节点故障模型,随机故障和故意攻击的熵功能。我们在两个故障模式中讨论随机网络模型和无垢网络模型的熵,故障强度从0逐渐增加到1.0。然后,当故障强度为0.3时,我们研究平均度分布与网络熵之间的关系。结果表明,网络的熵是可以合理地测量类似于网络中连接节点的网络的可靠性。该研究的目的是提供一种研究网络可靠性的新方法。

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