基于迁移学习的动态社区发现多目标遗传算法
Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection
摘要：
动态社区发现是近年来复杂网络和人工智能研究的热点和基本问题。当网络结构发生变化时，既要最大限度地提高聚类的准确性，又要最大限度地减少连续两次聚类结果之间的差异。这两个目标之间存在着一种权衡关系。本文在传统多目标进化算法框架的基础上，结合迁移学习，提出了一种基于特征迁移的多目标优化遗传算法。其主要思想是从过去的社区结构中提取稳定的特征，保留有价值的特征信息，并将这些特征信息集成到当前的优化过程中，以改进进化算法。此外，本文还提出了一个基于信息论的社区发现问题分析框架。然后，我们利用这个框架来证明TMOGA的合理性。实验结果表明，与现有动态网络社区发现算法相比，该算法在不同的测试问题中均能取得更好的聚类效果。
英文摘要：
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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