[1]姜斐[],王晓军[],许斌[],等. 基于粒子松密度的复杂网络社团划分算法[J].计算机技术与发展,2016,26(10):60-63.
　JIANG Fei[],WANG Xiao-jun[],XU Bin[],et al. Community Clustering Algorithm in Complex Networks Based on Bulk Density[J].,2016,26(10):60-63.
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 基于粒子松密度的复杂网络社团划分算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年10期
页码:
60-63
栏目:
智能、算法、系统工程
出版日期:
2016-10-10

文章信息/Info

Title:
 Community Clustering Algorithm in Complex Networks Based on Bulk Density
文章编号:
1673-629X（2016)10-0060-04
作者:
 姜斐[1]; 王晓军[1]; 许斌[2]; 亓晋[2]
1.南京邮电大学 计算机学院 软件学院；2.南京邮电大学 物联网学院
Author(s):
 JIANG Fei[1]; WANG Xiao-jun[1]; XU Bin[2]; QI Jin[2]
关键词:
 ; 复杂网络; 松密度; 社团 ; 挖掘
Keywords:
 ; complex ;  networks; bulk ;  density; community; clustering
分类号:
TP301.6
文献标志码:
A
摘要:
 复杂网络的社团挖掘算法是近几年数据挖掘领域新兴起的一个热点课题。传统的智能优化算法虽然在社团挖掘方面有较好的效果，但其执行效率低，适用范围窄；而已有的启发式算法虽然在社团挖掘效率方面的优势比较明显，但相比于智能优化算法，其普适性仍未得到改善。为综合提高社团划分算法的效率，通过对材料科学领域的松密度的概念进行调研，结合复杂网络的特有属性，提出一种基于节点松密度的社团挖掘算法。实验结果表明，相比于其他算法，该算法在时间和精度上都有较为显著的优势。
Abstract:
 The association clustering algorithm of complex networks is a new emerging hot topic in the field of data mining. Traditional intelligent optimization algorithm has better effect in clustering,but it has low execution efficiency and narrow application scope. Although some heuristic algorithm has obvious advantages of clustering efficiency,but compared with the intelligent optimization algorithm,univer-sality still has not be improved. To improve the efficiency of community division algorithm,through the research of the concept of bulk density in the field of materials science,puts forward a kind of association clustering algorithm based on bulk density. The experiment shows that the algorithm proposed has obvious advantage in time and precision compared with other algorithms.

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更新日期/Last Update: 2016-11-25