基于聚类效应节点吸引力的复杂网络模型

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。

Complex Network Model Based on Node Attraction with Clustering Effect

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.