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Dynamic community detection is the hotspot and basic problem of complexnetwork and artificial intelligence research in recent years. It is necessaryto maximize the accuracy of clustering as the network structure changes, butalso to minimize the two consecutive clustering differences between the tworesults. There is a trade-off relationship between these two objectives. Inthis paper, we propose a Feature Transfer Based Multi-Objective OptimizationGenetic Algorithm (TMOGA) based on transfer learning and traditionalmulti-objective evolutionary algorithm framework. The main idea is to extractstable features from past community structures, retain valuable featureinformation, and integrate this feature information into current optimizationprocesses to improve the evolutionary algorithms. Additionally, a newtheoretical framework is proposed in this paper to analyze community detectionproblem based on information theory. Then, we exploit this framework to provethe rationality of TMOGA. Finally, the experimental results show that ouralgorithm can achieve better clustering effects compared with thestate-of-the-art dynamic network community detection algorithms in diverse testproblems.

Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection