Target Group Influence Maximization using Reinforcement Learning Approach

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Binesh Neda; Haddadi Mahdieh; Arazkhani Niloofar

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

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Title

Target Group Influence Maximization using Reinforcement Learning Approach

Author(s)

Binesh Neda | Haddadi Mahdieh | Arazkhani Niloofar

Keywords

Target Group Influence Maximization

Social Network

Reinforcement Learning

Abstract

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.

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APA: Copy

Binesh, Neda, Haddadi, Mahdieh, & Arazkhani, Niloofar. (2024). Target Group Influence Maximization using Reinforcement Learning Approach. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147667/en

Vancouver: Copy

Binesh Neda, Haddadi Mahdieh, Arazkhani Niloofar. Target Group Influence Maximization using Reinforcement Learning Approach. 2024. Available from: https://sid.ir/paper/1147667/en

IEEE: Copy

Neda Binesh, Mahdieh Haddadi, and Niloofar Arazkhani, “Target Group Influence Maximization using Reinforcement Learning Approach,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147667/en

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