DNA: General Deterministic Network Adaptive Framework for Multi-Round Multi-Party Influence Maximization

The influence maximization problem has been considered a vital problem when companies provide similar products or services. Since there are limited resources, companies must determine a strategy to occupy as much market share as possible. In this paper, we propose a general Deterministic Network Adaptive (DNA) framework to solve the multi-round multi-party influence maximization problem. To obtain the most market share, using one single strategy to determine seed nodes is not sufficient in the long term. The reason is that the network status changes during the multi-round procedure. The strategies of selecting seed nodes in each round should depend on the current status of influence diffusion in the network. DNA framework leverages the concept of reinforcement learning to maximize the expected cumulative influence. In addition, the learning process is deterministic, so that it does not take time to explore the spaces that are less important. We further design a similarity function to measure the similarity between two networks. DNA framework can avoid redundant computation when the similar networks have been trained before. Moreover, we propose the method to make the policy decision to maximize the influence spread in coopetition scenario based on DNA framework. The proposed framework is evaluated with synthetic data and real-world data. From the experimental results, DNA framework outperforms the existing works in influence maximization problems. The coopetition policy which is generated by DNA has the best performance in most cases.