[1]王 聪,柴争义.基于多目标进化的复杂网络社区检测[J].计算机技术与发展,2020,30(06):44-48.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 009]
　WANG Cong,CHAI Zheng-yi.Complex Network Community Detection Based on Multi-objective Evolution[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):44-48.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 009]
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基于多目标进化的复杂网络社区检测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年06期
页码:
44-48
栏目:
智能、算法、系统工程
出版日期:
2020-06-10

文章信息/Info

Title:
Complex Network Community Detection Based on Multi-objective Evolution
文章编号:
1673-629X(2020)06-0044-05
作者:
王 聪; 柴争义
天津工业大学 计算机科学与技术学院,天津 300387
Author(s):
WANG Cong; CHAI Zheng-yi
School of Computer Science and Technology,Tianjin University of Technology,Tianjin 300387,China
关键词:
复杂网络社区; 多目标进化; 近邻传播(AP) 聚类; 模拟退火(SA) 算法
Keywords:
words: complex network community;  multi-objective evolution;  neighbor propagation clustering;  simulated annealing algorithm (SA)
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 06. 009
摘要:
为了准确地发现复杂社区结构,提出一种改进的多目标进化的复杂网络社区检测算法。 通过在某一范围内等间距产生多个 p 参数,再将其代入 AP 聚类算法通过半监督聚类方式确定聚类个数以及产生初始种群,克服传统的通过随机方式产生的初始解聚类效果不稳定的缺点,且用模拟退火(SA) 算法对多目标进化算法进行改进提高种群搜索能力,防止寻优过程陷入局部最优解。 分别在不同 u 值下仿真 40 次,以 Footbal 足球社交网络、Karate-Club 网络和 Dolphins 网络作为测试案例,与传统多目标进化算法以及基于近邻传播(AP)的多目标算法进行实验对比,结果表明文中提出的多目标进化算法在总体上 MNI 数值更大,即改进效果明显,因此可应用该算法对复杂网络社区进行更加精确的检测。
Abstract:
In order to accurately discover the complex community structure, we propose an improved multi-objective evolutionary complex network community detection algorithm. By generating multiple p-parameters at equal intervals in a certain range,and then substituting them into AP clustering algorit-hm, the number of clusters is determined and the initial population is generated by semi-supervised clustering,so as to overcome the disadvantages of unstable clustering effect of the initial solution of traditional random method. At the same time,the multi-objective evolutionary algorithm is impro-ved by simulated annealing (SA) algorithm to improve the population searching ability and prevent the optimization process from falling into the local optimal solution. Simulating 40 times under different u values respectively,and using Footbal,a football social network,Karate-Club network and Dolphins network as test cases,we compare the proposed algorithm with the traditional multi-objective evolutionary algorithm and the neighbor-based propagation multi-objective evolutionary algorithm. It is concluded that the improved multi-objective evolutionary algorithm has a larger MNI value in the whole,that is,the improvement effect is obvious. Therefore,it can be used to detect the complex network community more accurately.
更新日期/Last Update: 2020-06-10