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Dynamic Social Network Optimization via a Hybrid Genetic Algorithm with MLE-Enhanced Fitness
- 1 School of Science, North China University of Technology, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
Dynamic social networks present significant challenges for rapid identification of high-impact nodes, which is crucial for effective information dissemination in scenarios such as emergency management and enterprise marketing. This study introduces a hybrid optimization framework, MLE-GA, that integrates maximum likelihood estimation (MLE) with traditional genetic algorithms (GA) to address these challenges. In the proposed two-layer architecture, the outer GA layer performs a global search to optimize candidate seed node combinations, while the inner MLE layer dynamically estimates network parameters in real time, thereby constructing a probabilistic model that guides the evolution of the solution. Simulation experiments, including tests on synthetic datasets and real-world networks like Zachary’s Karate Club, demonstrate that MLE-GA achieves an error rate below 5% for identifying high-influence nodes, significantly outperforming conventional MLE approaches. The results confirm that the hybrid method not only effectively distinguishes between high- and low-influence nodes but also adapts to rapid changes in network structure, ensuring efficient resource allocation and robust optimization in dynamic environments. These findings underscore the potential of MLE-GA as a universal solution for complex social network problems where timely and accurate decision-making is imperative.
Keywords
Genetic algorithms, Maximum likelihood estimation, Node influence identification
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Cite this article
Zhang,J. (2025). Dynamic Social Network Optimization via a Hybrid Genetic Algorithm with MLE-Enhanced Fitness. Theoretical and Natural Science,105,47-60.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation
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