Research On The Influence Of Theme - Oriented Coupling

As the network has become the main social tool in the modern society, digging the most influential network users has become a heated issue. Network marketing has been the main method for products in the commercial battlefield. Taking sufficient advantages of the own effect of users is the key point to success. Thus, the maximization of social networks influence has become the focus of research. Identify a limited number of seed nodes in a social network should be the right way to maximize the influence, which could increase the greatest effect on the network.A lot of mature theories have been studied on this subject, such as the classic greedy algorithm influence maximization problem. However, the traditional influence maximization problem did not take into account the relationship between the information transmitted in networks of different themes, which has limited the solving accuracy of this issue.This study proposed the influence maximization problem subject-oriented coupling, and proposed GACT (Greedy Algorithm based on the Couped Topics) algorithm to solve this problem, which could also mine the most influential user power in the spread of a particular theme. GACT initially analysis coupled between a network of different topics, and then use latent semantic indexing method to calculate the user preferences for different themes, and explore independent extension cascade model in considering the coupling between the similarity of themes and user preference on the basis of different themes.Then use classical greedy algorithm to dig the most influential users on extended propagation model, and use CELF algorithm optimized to improve the time efficiency of the algorithm finally. Compared with the classical influence maximization algorithm, GACT propagation algorithm can take into account the coupling between themes and similarities,which is more effective in combination with user preferences, and it digs out more accurate seed nodes influence maximization problem. Taken together, GACT algorithm is more efficient in digging the most influential users in a particular theme compared to the classical influence maximization algorithm.