Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection
This work addresses the problem of dynamic community detection in complex networks for AI researchers, offering an incremental improvement by integrating transfer learning into existing evolutionary algorithms.
The paper tackles dynamic community detection by proposing a transfer learning-based multi-objective genetic algorithm (TMOGA) that extracts stable features from past community structures to improve clustering accuracy and minimize differences between consecutive results, achieving better performance than state-of-the-art algorithms in diverse test problems.
Dynamic community detection is the hotspot and basic problem of complex network and artificial intelligence research in recent years. It is necessary to maximize the accuracy of clustering as the network structure changes, but also to minimize the two consecutive clustering differences between the two results. There is a trade-off relationship between these two objectives. In this paper, we propose a Feature Transfer Based Multi-Objective Optimization Genetic Algorithm (TMOGA) based on transfer learning and traditional multi-objective evolutionary algorithm framework. The main idea is to extract stable features from past community structures, retain valuable feature information, and integrate this feature information into current optimization processes to improve the evolutionary algorithms. Additionally, a new theoretical framework is proposed in this paper to analyze community detection problem based on information theory. Then, we exploit this framework to prove the rationality of TMOGA. Finally, the experimental results show that our algorithm can achieve better clustering effects compared with the state-of-the-art dynamic network community detection algorithms in diverse test problems.