论文标题
表征增长网络的学位分布的头脑
Characterizing the head of the degree distributions of growing networks
论文作者
论文摘要
本文中的分析有助于解释增长的网络的形成，该网络具有遵循扩展指数或幂律尾巴的程度分布。我们提出了一个通用模型，其中边缘动力学是由新节点的连续附件和触发随机或优先附件的混合附件机制驱动的。此外，根据响应机制建立了新添加的节点的相互边缘。所提出的框架通过允许根据各种离散概率分布（包括Poisson，Binomial，Zeta和Log-series）变化的新边缘数量来扩展先前的混合附件模型。我们得出了由混合附件和响应机制产生的极限内分布的分析表达式。此外，我们描述了累积内分布的动力学的演变。仿真结果说明了新边缘的数量和互惠过程如何显着影响学位分布的负责人。
The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact the head of the degree distribution.
