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.
翻译:动态社区探测是近年来复杂的网络和人工智能研究的热点和基本问题。 有必要随着网络结构的变化最大限度地提高集群的准确性, 但也有必要尽可能缩小两个结果之间的连续组合差异。 这两个目标之间存在着一种权衡关系。 在本文中, 我们基于转移学习和传统的多目标进化演化算法框架, 提出了一个基于转移学习和传统多目标进化算法框架的功能转移多目标优化遗传解算法(TMOGA ) (TMOGA ) 。 主要想法是从过去的社区结构中提取稳定特征, 保留有价值的特征信息, 并将这一特征信息纳入当前的优化进程, 以改进进化算法。 此外, 本文还提出了一个新的理论框架, 以基于信息理论分析社区检测问题。 然后, 我们利用这个框架来证明TMOGA的合理性。 最后, 实验结果显示, 我们的算法可以比在各种测试问题中最先进的动态网络社区检测算法更好地实现组合效应 。