文献类型：期刊文献

中文题名：改进多目标五行环优化的重叠社区发现算法

英文题名：Improved multi-objective five-element cycle optimization algorithm for overlapping community detection

作者：何烈芝[1];刘漫丹[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2022

卷号：43

期号：7

起止页码：1915

中文期刊名：计算机工程与设计

外文期刊名：Computer Engineering and Design

收录：CSTPCD;;北大核心:【北大核心2020】；

语种：中文

中文关键词：社区发现;重叠社区;启发式算法;多目标优化;五行环优化

外文关键词：community detection;overlapping community;heuristic algorithm;multi-objective optimization;five-element cycle optimization

摘要：针对多目标五行环优化的重叠社区发现算法社区发现质量不高的缺陷,提出一种改进的启发式算法,在原算法的基础上采用新的个体表达方式和解码方式来提高进化效率,改用部分匹配交叉算子和基本位变异算子以保证种群的多样性。实验结果表明,在人工合成网络和真实社会网络上,改进算法的社区发现质量要明显好于原算法,与其它不同的重叠社区发现算法相比,该算法也能够得到结构强度和准确率较好的重叠社区划分,验证了改进算法的有效性。
To improve the quality of overlapping community detection in multi-objective five-element cycle optimization for overlapping community detection,an improved heuristic algorithm was presented.On the basis of the original algorithm,an individual expression and decoding process were used to improve the evolution efficiency,and partial-mapped crossover operator and basic bit mutation operator were used to ensure the diversity of the population.Results of experiments on LFR benchmark networks and real-world networks show that the quality of community detection of the improved algorithm is obviously better than that of the original algorithm.Compared with other overlapping community detection algorithms,the algorithm can obtain overlapping community divisions with good structure strength and accuracy,the effectiveness of the improved algorithm is verified.