Topic Based Time-Constrained Influence Modeling And System Implementation

In recent years,with the rapid development of Internet related technologies,a large number of online social networks,such as Twitter and Facebook,have risen and boomed largely.Online social networks have provided people with an online platform to build and maintain social relations,through which people can show themselves and communicate with each other.As a very representative example,Twitter stands out for its efficient,real-time,and simple features,and is becoming more and more favored by users.Huge user group and information spread characteristic make online social network a perfect platform for viral marketing,and the information spread issue in online social networks has been widely studied by scholars.Among them,influence maximization is the problem of excavating the most influential user groups in online social networks through analyzing the information spreading characteristics.This paper establishes a dynamic influence propagation model based on topic distribution.On the basis of this,this paper excavates the most influential user node collection in the limited time.First of all,based on the online social network data obtained through Twitter open API,we integrated topic and time features into traditional influence maximization problem and established a dynamic influence propagation model,which considers both user's online time feature and users' time delay of information delivery.Secondly,according to the above dynamic influence propagation model,this paper extracts the features included in the model,including topic-based influence probability between users and users' topic-based propagation time delay.After that,a user influence estimation model is build based on the information propagation rule excavated in online social network.Finally,the most influential seed set within limited time is mined out of the online social network.Based on the propagation regularity and influence attenuation rule in social network,we designed both greedy and heuristic algorithms to excavate seed set.And an impact evaluation algorithm is designed to evaluate the effectiveness and operational efficiency of above algorithms.The experiment shows that the algorithms presented in this paper can effectively excavate the most influential seed set in limited time.At last,a Topic-based Timecritical Influence Maximization(TTIM)system is designed and implemented.