基于聚类效应节点吸引力的复杂网络模型

Complex Network Model Based on Node Attraction with Clustering Effect

针对原始吸引模型及改进模型存在聚集系数小的缺陷,提出一种基于聚类效应节点吸引力的复杂网络模型CALW。该模型针对真实网络中择优连接的局域性特点,借鉴森林火灾传播的思想构造局域世界,将节点的吸引力定义为随时间变化的函数。数值模拟结果表明,CALW模型的度分布服从幂律分布,具有较高的网络聚集系数,且有保持高聚集性不变的特性。

Aiming at the disadvantage of low clustering coefficient in initial attractive model proposed by Dorogovtsev and its extended model,a complex network evolving model based on node attraction with clustering effect（CALW model） is proposed.According to the local-world property of preferential attachment existed in real networks,CALW model constructs local-world by referencing on the idea of forest fire,and defines node attraction as a dynamic function with the change.Simulation results show that the network generated by CALW model follows power-law degree distribution.