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针对目前网络仿真常用的Waxman随机网络拓扑模型存在的网络节点疏密不当、度数难以控制等问题,提出了一种基于K均值聚类的随机图拓扑生成算法KRT和一种基于K均值聚类的层次结构拓扑生成算法KHT。仿真实验表明使用基于K均值聚类的随机网络和层次结构拓扑生成器得到的网络拓扑图避免了两个节点间距离过近的情况发生,节点分布均匀且疏密得当,边的分布也比较均衡。
In order to solve the problems that the Waxman stochastic network topology model commonly used in network simulation has some problems such as improper network node density and difficult degree control, a new random graph topology generation algorithm based on K-means clustering (KRT) and a K- Hierarchy topology generation algorithm. Simulation results show that the network topology obtained by random network based on K-means clustering and hierarchical topology generator avoids the situation that the distance between two nodes is too close, the nodes are distributed evenly and densely, and the distribution of edges is relatively balanced .