RLIM: representation learning method for influence maximization in social networks

A core issue in influence propagation is influence maximization, which aims to find a set of nodes that maximize influence spread by adopting a specific information diffusion model. The limitation of the existing algorithms is they excessively depend on the information diffusion model and randomly set the propagation ability. Therefore, most algorithms are difficult to apply in large-scale social networks. A method to solve the problem is neural network architecture. Based on the architecture, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The algorithm consists of three main parts: the influence cascade of each source node is the premise; the multi-task deep learning neural network to classify influenced nodes and predict propagation ability is the fundamental bridge; the prediction model applying to the influence maximization problem by the greedy strategy is the purpose. Furthermore, the experimental results show that the RLIM algorithm has greater influence spread than the state-of-the-art algorithms in different online social network datasets, and the information diffusion is more accurate.