Impact of Online Community Structure on Information Propagation:Empirical Analysis and Modeling  

作　　者：Zhong He Lian-Ren Wu Xia Chen Ting-Jie Lu 

机构地区：[1]School of Economics and Management,Beijing University of Post and Telecommunications

出　　处：《Journal of Harbin Institute of Technology(New Series)》2013年第3期124-128,共5页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Basic Research Program of China (973 Program) (Grant No. 2012CB315805);National Natural Science Foundation ofChina (Grant No. 71172135)

摘　　要：Online social networking sites ( OSNS) ,as a popular social media platform,have been developed massively for business and research purposes. In this paper,it investigated the impact of community structure in online social network on information propagation. A SI (Susceptible-Infected) model based on community structure was proposed. In the SI model,the heterogeneity of user's active time was taken into account. From the results,it was found that the number of links among communities determines the fraction of infected nodes. With the increase of the number of groups G,however,the fraction of infected nodes remains approximately constant. The simulation results will be of great significance: the information will last relatively short for group networks which have either a small or a large number of groups. The results can be useful for optimizing or controlling information,such as propagating rumors in online social networks.Online social networking sites （ OSNS） ,as a popular social media platform,have been developed massively for business and research purposes. In this paper,it investigated the impact of community structure in online social network on information propagation. A SI （Susceptible-Infected） model based on community structure was proposed. In the SI model,the heterogeneity of user＇s active time was taken into account. From the results,it was found that the number of links among communities determines the fraction of infected nodes. With the increase of the number of groups G,however,the fraction of infected nodes remains approximately constant. The simulation results will be of great significance： the information will last relatively short for group networks which have either a small or a large number of groups. The results can be useful for optimizing or controlling information,such as propagating rumors in online social networks.

关 键 词：community structure information propagation user behavior HETEROGENEITY 

分 类 号：TN92[电子电信—通信与信息系统]