A Learning-based Framework to Handle Multi-round Multi-party Influence Maximization on Social Networks

Abstract

Considering nowadays companies providing similar products or services compete with each other for resources and customers, this work proposes a learning-based framework to tackle the multi-round competitive influence maximization problem on a social network. We propose a data-driven model leveraging the concept of meta-learning to maximize the expected influence in the long run. Our model considers not only the network information but also the opponent's strategy while making a decision. It maximizes the total influence in the end of the process instead of myopically pursuing short term gain. We propose solutions for scenarios when the opponent's strategy is known or unknown and available or unavailable for training. We also show how an effective framework can be trained without manually labeled data, and conduct several experiments to verify the effectiveness of the whole process.