Transfer Learning Based Multi-Objective Evolutionary Algorithm for Community Detection of Dynamic Complex Networks

• URL: http://arxiv.org/abs/2109.15136v1
• Date: Thu, 30 Sep 2021 17:16:51 GMT
• Title: Transfer Learning Based Multi-Objective Evolutionary Algorithm for Community Detection of Dynamic Complex Networks
• Authors: Jungang Zou, Fan Lin, Beizhan Wang, Siyu Gao, Gaoshan Deng, Wenhua Zeng, Gil Alterovitz
• Abstract summary: We propose a Feature Transfer Based Multi-Objective Optimization Algorithm (TMOGA) based on transfer learning and traditional multi-objective evolutionary algorithm framework. We 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.
• Score: 1.693830041971135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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