Addressing Competitive Influence Maximization on Unknown Social Network with Deep Reinforcement Learning

Abstract

Recent studies have considered the reinforcement and deep reinforcement learning models to address the competitive influence maximization (CIM) problem. However, these models assume complete network topology information is available to address the CIM problem. This assumption is unrealistic as it is difficult to obtain complete social network data and requires exhaustive efforts to obtain it. In this work, we propose a deep reinforcement learning-based (DRL) model to tackle the competitive influence maximization on unknown social networks. Our proposed model has a two-fold objective: the first is to identify the time when to explore the network to collect network information. The second is to determine key influential users from the explored network, using optimal seed-selection strategy considering the competition in the social network. Moreover, we integrate the transfer learning in DRL to improve the training efficiency of DRL models. Experimental results show that our proposed DRL and transfer learning-based DRL models achieve significantly better performance than heuristic-based methods.