文章基本信息

标题：Community Detecting and Feature Analysis in Real Directed Weighted Social Networks
本地全文：下载
作者：Liu, Yao ; Liu, Qiao ; Qin, Zhiguang 等
期刊名称：Journal of Networks
印刷版ISSN：1796-2056
出版年度：2013
卷号：8
期号：6
页码：1432-1439
DOI：10.4304/jnw.8.6.1432-1439
语种：English
出版社：Academy Publisher
摘要：Real social networks usually have some structural features of the complex networks, such as community structure, the scale-free degree distribution, clustering, "small world" network, dynamic evolution and so on. A new community detecting algorithm for directed and weighted social networks is proposed in this paper. Due to the use of more reference information, the accuracy of the algorithm is better than some of the typical detecting algorithms. And because of the use of heap structure and multi-task modular architecture, the algorithm also got a high computational efficiency than other algorithms. The effectiveness and efficiency of the algorithm is validated by experiments on real social networks. Based on the theories and models of complex networks, the features of the real large social networks are analyzed.
关键词：modularity;degree distribution;clustering coefficient;hierarchy