摘要： 针对移动社交网络的动态性、用户不同重要性和信息交互有向性,基于4 种初始网络提出能准确描述移动社交网络结构的拓扑模型。采用随机游走理论和改进的PageRank 算法,引入过渡概率使每两时步之间的网络拓扑结构相互联系。通过PageRank 算法得到节点的势,进而求出概率过渡矩阵,利用随机游走理论由上一时步边存在概率矩阵和概率过渡矩阵得到当前时步边存在概率矩阵,每一时步动态地增加一个节点并检验是否有离开的节点。仿真结果显示,该模型在4 种初始网络下得到的网络拓扑结构,入度、出度、势分布以及度- 势相关性均具有明显幂律特性,表明随机游走理论和改进的PageRank 算法能较准确描述移动社交网络,具有一定的实践意义。

关键词: 社交网络, 网络拓扑, 随机游走, PageRank 算法, 过渡概率, 仿真模型

Abstract: A topological model that can describe the mobile social network accurately is proposed based on four initial networks considering the dynamic of social network,the different importance of users and the direction of information interaction. Random walking theory and improved PageRank algorithm are adopted,and transition probability is introduced to associate the network topological structure between two time-steps. Firstly,PageRank algorithm is used to obtain the strength of the nodes in order to get the probability transition matrix. Then random walking theory is used to get the current time-step edge existence probability matrix based on the last time-step edge existence probability matrix and the probability transition matrix. During each time-step,a node is added and it is checked if there is any departure node. Finally,simulation model is used to simulate the four initial networks in in-degree,out-degree,strength distribution and the correlation between degree and strength. The results indicate that the four initial networks’ in-degree,out-degree,strength distribution and the correlation between degree and strength show obvious power-law character. It shows that the random walking theory and improved PageRank algorithm can describe the mobile social network better,which is of certain practical significance.

Key words: social network, network topology, random walking, PageRank algorithm, transition probability, simulation model

中图分类号: 

• TP393