An Algorithm based on Efficient Influence Maximization applied to Social Network

The Social Network is becoming more and more common. This has also caused the wave to find influence maximization problem by the academic community. The earliest Influence maximization problem was proposed by Kempe. Kleinberg and Tardosy in 2003. Given a social network G and a constant k, finding k nodes in G which can influence most nodes in the diffusion model. As the Social Network is getting larger, the previous algorithms in this case, whether in the IC or the LT Model, take more time to execute, and these algorithms do not have good Scalability. The method in this paper given a deterministic value for edges and develops an efficient algorithm. It not only can reduce the time required for implementation, but also ensures the accuracy of results. And the experimental results show that our algorithm is better than the previous algorithm, and has good scalability.