  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.
