基于节点吸引力的可调参数复杂网络模型

针对真实网络的生长演化规律,以及BA无标度网络模型和原始的节点吸引力模型在择优连接以及生成网络统计特征方面所存在的问题,综合考虑复杂网络生长演化过程中节点度和节点吸引力的择优连接特性,提出了一种基于节点吸引力的可调参数复杂网络模型. 理论研究与仿真实验分析表明,基于节点吸引力的可调参数复杂网络模型可以有效生成结构稳定并与实际网络统计特征很接近的复杂网络,通过调节模型参数可以灵活调整网络的生长演化过程. 模型生成的网络度分布仍然服从幂律分布,并且具有较高的群集系数和平均路径长度. 

Complex Network Model Based on Node Attraction with Tunable Parameters

According to the evolution rule of real-world networks and aiming at the problems existing in preferential attachment and statistical characteristics of network in BA model and initial attractive model, taking into account preferential attachment of node degree and node attraction in the evolution process of complex network, a complex network model based on node attraction with tunable parameters was proposed. Theory research and simulation analysis show the fact that the complex network model based on node attraction with tunable parameters can effectively generate networks with stable structure and actual statistical characteristics. Specifically, the degree distribution of networks still follows power-law distribution, while clustering coefficient and average path length are high.