Target Group Influence Maximization using Reinforcement Learning Approach

• Published in: International Conference on Web Research (Online) pp. 130 - 136
• Main Authors: Binesh, Neda, Haddadi, Mahdieh, Arazkhani, Niloofar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.04.2024
• Subjects: Benchmark testing; Network analyzers; Q-learning; Reinforcement Learning; Social Network; Social networking (online); Target Group Influence Maximization; Task analysis
• ISSN: 2837-8296
• DOI: 10.1109/ICWR61162.2024.10533351

In social networks groups play a crucial role and making decisions based on majority consensus. Which influencer nodes should we select if our goal is to broadcast a subject in a target group and increase the number of active nodes in this group? Here, we study a new influence maximization (IM) problem that focuses on individuals in a target group who are activated by some relevant topic or information. Target Group Influence Maximization (TGIM) aims to select k influencer nodes in such a way that the number of activated nodes in the target group is maximized. In this paper, we study TGIM and focus on activating the majority of nodes in the target group. We propose an algorithm named Reinforcement Learning for Target Group (RLTG) based on the analysis of the influence of nodes on the target group. The algorithm uses the reinforcement learning approach to learn the optimal path from each target node to some candidate influencers. The experimental results indicate that the recommended approach outperforms known methods.