NEW METHOD TO ESTIMATE SCALING OF POWER-LAW DEGREE DISTRIBUTION AND HIERARCHICAL NETWORKS

A new method and corresponding numerical procedure are introduced to estimate scaling exponents of power-law degree distribution and hierarchical clustering func tion for complex networks. This method can overcome the biased and inaccurate faults of graphical linear fitting methods commonly used in current network research. Furthermore, it is verified to have higher goodness-of-fit than graphical methods by comparing the KS (Kolmogorov-Smirnov) test statistics for 10 CNN (Connecting Nearest-Neighbor)networks.     

A difference equation approach to statistical mechanics of complex networks

In this paper, we propose a difference equation approach to the estimation of the degree distributions in growing networks after having analyzed the disadvantages of some existing approaches. This approach can avoid logic conflicts caused by the continuum of discrete problems, and does not need the existence assumption of the stationary degree distribution in the network analysis. Using this approach, we obtain a degree distribution formula of the Poisson growth and preferential attachment network. It is rigorously shown that this network is scale-free based on the Poisson process theory and properties of F-distribution.     

不确定性参数系统振动控制闭环特征值的上、下界估计

用凸模型理论讨论参数不确定性系统的振动控制问题。把不确定系统的振动控制转化为确定性问题来处理,然后讨论不确定参数对闭环特征值的影响,提出了闭环系统特征值上、下界的计算公式。