Graph Influence Maximization Algorithm Based on Reinforcement Learning-PPO Algorithm

Information dissemination in social network has gained great attention in the study of complex networks. In this paper, we propose a novel network influence maximization algorithm incorporating graph representation learning and reinforcement learning, called RL-PPO, to select a set of seed nodes as the starter nodes and by which the information can be propagated as fast as could in the network,i.e., reaching the status of influence maximization. This algorithm take the feature matrix and adjacency matrix of the network as inputs, and the seed nodes as outputs.To better represent the network, the graph representation learning method is employed to extract network features and to perform feature aggregation. The experiment results show that, compared with the traditional greedy algorithm, the seed nodes determined by this proposed algorithm GraphPPO exhibited higher propagation efficiency.