论文标题
使用自适应遗传算法和基于节点相似性编码来检测复杂网络中的社区
Detecting Communities in Complex Networks using an Adaptive Genetic Algorithm and node similarity-based encoding
论文作者
论文摘要
检测复杂网络中的社区可以阐明建模现象的基本特征和功能。这个主题吸引了来自学术界和行业的各个领域的研究人员。在实施社区检测的不同方法中，遗传算法（GA）最近变得流行。考虑到当前使用的基于基因座和基于解决方案 - 向量的编码的缺点，以表示个人，在本文中，我们建议（1）一种基于节点相似性的新编码方法，以表示网络分区为一个基于MST的个人。然后，我们提出（2）一种用于社区检测的新型自适应遗传算法，以及（3）新的初始人口产生功能，以及（4）一种称为基于正弦突变功能的新的自适应突变函数。使用提出的方法，我们将基于相似性的基于相似性和基于模块化的方法结合在一起，以在进化框架中找到复杂网络的社区。除了提出的表示方案可以避免毫无意义的突变或断开社区的事实外，我们还表明，新的初始人口产生功能以及新的自适应突变函数可以改善算法的收敛时间。与几种经典和最新算法相比，实验和统计测试验证了所提出方法的有效性。
Detecting communities in complex networks can shed light on the essential characteristics and functions of the modeled phenomena. This topic has attracted researchers of various fields from both academia and industry. Among the different methods implemented for community detection, Genetic Algorithms (GA) have become popular recently. Considering the drawbacks of the currently used locus-based and solution-vector-based encodings to represent the individuals, in this paper, we propose (1) a new node similarity-based encoding method to represent a network partition as an individual named MST-based. Then, we propose (2) a new Adaptive Genetic Algorithm for Community Detection, along with (3) a new initial population generation function, and (4) a new adaptive mutation function called sine-based mutation function. Using the proposed method, we combine similarity-based and modularity-optimization-based approaches to find the communities of complex networks in an evolutionary framework. Besides the fact that the proposed representation scheme can avoid meaningless mutations or disconnected communities, we show that the new initial population generation function, and the new adaptive mutation function, can improve the convergence time of the algorithm. Experiments and statistical tests verify the effectiveness of the proposed method compared with several classic and state-of-the-art algorithms.
