Positive Influence Maximization in Signed Networks Within a Limited Time

With the rapid development of science and technology, influence maximization (IM) problem has been a hot research issue. There are positive and negative relations in social networks, so the problem of IM in signed networks has a wide range of applications. Moreover, the dissemination of information is usually time-sensitive in social networks. Therefore, in this article, we propose a problem about maximizing the positive influence in signed networks within a limited time (PIMST), further we utilize the influence path to calculate the influence probability and propose an algorithm that is based on forwarding index and inverted index. In the proposed algorithm, we first select candidate seed nodes by combining two effective heuristic methods. Then, we design an algorithm to get the activation probability between node pairs in social networks. At last, we devise a method which combining forward index, inverted index with the idea of cost delay, this method speed up the selection process of seed nodes. Finally, the experimental results on three social network datasets illustrate the effectiveness of our approach compared with other algorithms.