Competitive Influence Maximization in Trust-Based Social Networks With Deep Q-Learning

Abstract

Social network analysis is a rapidly evolving research area having several real-life application areas, e.g. digital marketing, epidemiology, spread of misinformation. Influence maximization aims to select a subset of nodes in such manner that the information propagated over the network is maximized. Competitive influence maximization, which describes the phenomena of multiple actors competing for resources within the same infrastructure, can be solved with a greedy approach selecting the seed nodes utilizing the influence strength between nodes. Recently, deep reinforcement learning methods were applied for estimating the influence strength. We train a controller with reinforcement learning for selecting a node list of given length as the initial seed set for the information spread. Our experiments show that deep Q-learning methods are suitable to analyze the competitive influence maximization on trust and distrust based social networks. Received by the editors: 1 March 2023. 2010 Mathematics Subject Classification. 68T05. 1998 CR Categories and Descriptors. G.2.2 Discrete Mathematics: Graph Theory – Network problems; G.3. Probability and Statistics: Markov Processes; I.2.6 Artificial Intelligence: Learning – Connectionism and neural nets.

Keywords

• influence maximization
• reinforcement learning
• Q-learning