[1]杨晓光,朱保平.基于复杂网络的社区发现算法[J].南京理工大学学报(自然科学版),2016,40(03):267.[doi:10.14177/j.cnki.32-1397n.2016.40.03.003]
 Yang Xiaoguang,Zhu Baoping.Community detection algorithm based on complex network[J].Journal of Nanjing University of Science and Technology,2016,40(03):267.[doi:10.14177/j.cnki.32-1397n.2016.40.03.003]
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基于复杂网络的社区发现算法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年03期
页码:
267
栏目:
出版日期:
2016-06-30

文章信息/Info

Title:
Community detection algorithm based on complex network
文章编号:
1005-9830(2016)03-0267-05
作者:
杨晓光朱保平
南京理工大学 计算机科学与工程学院,江苏 南京 210094
Author(s):
Yang XiaoguangZhu Baoping
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
复杂网络 社区发现 中心节点 局部模块度 节点吸引力 孤立节点 重叠社区节点
Keywords:
complex network community detection central nodes local modules node attraction isolated nodes overlapping community nodes
分类号:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2016.40.03.003
摘要:
针对现有社区发现算法准确度较低的问题,该文提出了1种基于中心节点的社区发现算法。通过各节点度数及节点间相似度寻找社区的中心节点,然后利用局部模块度对各个社区进行优化,并根据节点吸引力将孤立节点和重叠社区节点尽量归入其社区,从而获得整个网络的社区划分。将该文算法分别与3种局部社区发现算法、4种全局社区发现算法相比较,实验结果表明,该算法可以提高社区发现的准确度,具有可行性。
Abstract:
To solve the problem of low accuracy of existing community detection methods,a community detection algorithm based on central nodes is proposed here.Central nodes of communities are found through the degree of each node and the similarity of nodes.Each community is optimized using local modules.The community division of the entire network is obtained by classifying isolated nodes and overlapping community nodes to their community as far as possible based on node attraction.The algorithm proposed here is compared with three local community detection algorithms and four global community detection algorithms respectively.Experimental results show that the algorithm can improve the accuracy of the community detection and is feasible.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2015-12-21 修回日期:2016-01-18
作者简介:杨晓光(1991-),男,硕士生,主要研究方向:复杂网络,E-mail:295183327@qq.com。
引文格式:杨晓光,朱保平.基于复杂网络的社区发现算法[J].南京理工大学学报,2016,40(3):267-271.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-06-30