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.
In the framework of heterogeneous wireless networks,it is difficult for every user to obtain QoS-based services anywhere at any time.Due to heterogeneous networks,the dynamic network selection scheme needs to achieve seamless mobility,and also supports the optimization of service quality and load balancing.According to different business characteristics,this paper describes different real-time businesses in utility functions,and solves network selection problems for real-time businesses.Based on auction mechanism,it introduces the upset price in order to maximize online profits.Meanwhile,the network selection scheme is also helpful to control network congestion.The study of real-time business network selection based on auction mechanism can not only meet the demands of service quality of multiple realtime applications,but also achieves load balancing between different networks.
