Caveman网络及其在复杂网络熵分析中的应用

熵可以有效反映复杂系统内网络结构的异质性.针对熵指标在刻画网络全局异构上是否适用这一问题,目前仍缺少用以评测的基准网络.对此,在已有结构熵研究的基础上,提出一种Caveman网络构造及其演化规则,为网络复杂性的度量提供新的思路。通过数理分析和仿真实验验证该Caveman网络可以有效评测各类结构熵指标对其演化过程的敏感性,反映熵指标对网络复杂特征识别能力的差异。同时由于Caveman网络可以更好地探索信息空间和抵御攻击,将有助于设计鲁棒、高效的系统结构。

Caveman network and its application in analysis of complex network entropy

Entropy can effectively reflect the network structure heterogeneity of complex systems. For the question of applicability of entropy indices to describe the global heterogeneity of the network, the benchmark networks for the evaluation are still lacking. On the foundation of previous study, this paper introduces a Caveman network and its evolution rules, which provides a new way of thinking for measurement of network complexity. The theoretical analysis and simulation experiments indicate that the Caveman network can effectively evaluate the sensitivity of different structure entropies on evolution process of Caveman network, and reflect the difference of ability to identify the properties of network complex of entropy indices. Besides that, Caveman network can promote exploration in information space and resist network attacks, shedding new light on designing system structure with high robustness and efficiency.