Study on Temporal Influence Maximization Driven by User Behavior

Abstract

Influence maximization(IM) aims to find a group of users in a social network,through whom information can spread most widely in the network.Existing studies mainly focus on the IM problem in static networks.However,social networks are constantly evolving in real life,and propagation models(such as independent cascading model and linear threshold model) based on static networks are not suitable for the information propagation process in evolving networks.Meanwhile,the existing researches ignore the influence of user behavior on information propagation.Therefore,to tackle this problem,this paper proposes a behavior driven independent cascade(BDIC) propagation model,which can effectively describe the information propagation process in the evolving social networks.Based on this model,a user behavior-driven IM algorithm is proposed.It mainly includes three steps.Firstly,the process of message transmission is modeled to calculate the probability of information transmission in evolving social networks.Then,a user behavior-driven reverse influence sampling algorithm is proposed,which can effectively query the most influential user with a specific time.Finally,a seed query algorithm under different time(time series) is designed,which can effectively reflect the dynamic change characteristics of seed nodes in evolving social networks.To evaluate the effectiveness of the proposed algorithm,a similarity comparison method between seed nodes and the affected nodes is designed.Experiments on real datasets verify the efficiency and scalability of the proposed approaches.The results also demonstrate that the BDIC model can effectively reflect the information propagation process in evolving social networks.

Keywords

• evolving social networks|user behavior driven model|influence maximization|propagation probability matrix|reverse reachable set