基于社团结构的节点的影响力分析  

Analysis of influential node based on community structure

作　　者：朱晓霞[1] 赵雪[1] 刘萌萌[1] Zhu Xiaoxia Zhao Xue Liu Mengmeng(School of Economics ＆ Management, Yanshan University, Qinhuangdao Hebei 066004, Chin)

机构地区：[1]燕山大学经济管理学院,河北秦皇岛066004

出　　处：《计算机应用研究》2017年第9期2582-2585,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(71301140);河北省自然科学基金资助项目(G2015203425)

摘　　要：复杂网络中最具影响力节点的识别对网络动力学如加速信息的扩散或抑制流言的传播都具有重要影响意义。为了对节点影响力给出具体排序,在已有的各种最具影响力节点识别方法的基础上,提出了一种基于社团结构和k-shell节点法的节点影响力识别方法。其基本思想是利用某个节点处于不同社团的邻居节点的ks值判断节点影响力(称为Nc值),以识别ks值相同的节点的不同影响力。通过单感染源传染的SIR模型进行仿真,发现Nc值较高的节点不仅最终节点的影响范围较大,传播速度也快于其他节点。The identification of the most influential nodes in complex networks is of great significance to the network diffusion dynamics, such as the acceleration of information diffusion or the suppression of the spread of rumors. On the basis of the existing methods of identifying the most influential nodes,this paper proposed a recognition method of influential nodes based on community structure and k-shell decomposition analysis. The method used ks values of the neighbor nodes in different com- munities to determine the influence of nodes （called Nc value） ,identified the different influence of the nodes with the same ks value. And through the SIR model of single infection source transmission simulation, it is found that the node with the higher Nc value not only obtains the greater impact, but also spreads faster than other nodes.

关 键 词：复杂网络 社团结构 影响力 

分 类 号：TP393.02[自动化与计算机技术—计算机应用技术]