Electronic and Cyber Defense

Electronic and Cyber Defense

An Improved Method for Dynamic Opinion Maximization Based on Greedy Genetic Algorithms

Document Type : Original Article

Authors
Hossein Rayatparvar 1
Mohammad Hasani Ahangar 2
Aboulfazl Sarkardei 3
1 Master's degree, Imam Hossein University, Tehran, Iran
2 Professor, Imam Hossein University, Tehran, Iran
3 PhD student, Imam Hossein University (AS), Tehran, Iran
10.47176/ECDJ.2025.1617
Abstract
The analysis of online social network data represents a significant scientific challenge in contemporary research. Within the domain of user opinion analysis in these networks, dynamic opinion maximization has emerged as a nascent field of study. Prior investigations in this area have predominantly operated under the assumption of static, unchanging node opinions. Furthermore, the temporal evolution of user perspectives has received comparatively limited attention. This paper proposes a method for the dynamic opinion maximization problem, based on a greedy genetic algorithm, explicitly considering the dynamics of opinions and their evolution over time.The proposed method comprises two principal components: an activated opinion dynamics model and a seed node selection process. The activated opinion dynamics model is constructed by integrating the linear threshold model with stateless Q-learning, thereby explicitly accommodating the temporal fluctuations in opinions. A greedy genetic algorithm is employed for the selection of seed nodes. Following the identification of an initial seed node, the activated opinion dynamics model is initiated. During this phase, the seed node and its immediate neighbors are activated according to the linear threshold model. Subsequently, stateless Q-learning is utilized to update the opinions of the nodes. This iterative process continues until predefined termination criteria are satisfied. Experimental results on four signed social network datasets demonstrate that the proposed framework outperforms the state-of-the-art method by 14% in terms of the number of activated nodes and 27% in terms of average positive opinions.

Highlights

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Keywords
Online social network analysis dynamic opinion maximization seed nodes genetic algorithm Q
learning
Subjects
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Electronic and Cyber Defense
Volume 13, Issue 4 - Serial Number 52
Winter
Winter 2026
Pages 41-62

  • Receive Date 18 October 2025
  • Revise Date 18 November 2025
  • Accept Date 28 November 2025
  • Publish Date 22 December 2025