RLIM: Representation Learning Method for Influence Maximization in social networks

Abstract A core issue in influence propagation is influence maximization, which aims to find a group of nodes under a specific information diffusion model and maximize the final influence of this group of nodes. The limitation of the existing researches is that they excessively depend on the information diffusion model and randomly set the propagation ability (probability). Therefore, most of the algorithms for solving the influence maximization problem are basically difficult to expand in large social networks. Another challenge is that fewer researchers have paid attention to the problem of the large difference between the estimated influence spread and the actual influence spread. A measure to solve the influence maximization problem is applying advanced neural network architecture also represents learning method. Based on this idea, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The premise of this algorithm is to construct the influence cascade of each source node. The key is to adopt neural network architecture to realize the prediction of propagation ability. The purpose is to apply the propagation ability to the influence maximization problem by representation learning. Furthermore, the results of the experiments show that RLIM algorithm has greater diffusion ability than the state-of-the-art algorithms on different online social network data sets, and the diffusion of information is more accurate.