1. 西安交通大学人口与发展研究所,西安,710049
2. 斯坦福大学人口与资源研究所, 94305, 美国斯坦福
网络首发:2007-06-10,
纸质出版:2007
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杜海峰 1, 2, 李树茁 1, 等. 基于先验知识与模块性的网络社区结构探测算法[J]. 西安交通大学学报, 2007,41(6):750-754.
杜海峰 1, 2, 李树茁 1, et al. Detecting Algorithm Based on Prior Knowledge and Modularity for Networked Community Structure[J]. 2007, 41(6): 750-754.
在分析模块性指标和Newman有关网络社区结构探测算法的基础上
提出了一种基于先验知识与模块性的社区结构探测算法.利用节点度等社会网络结构先验知识
获得一个社区结构的基本划分
然后进行社区的合并
以此获得一个清晰的社区结构.经计算机模拟网络、Ucinet软件网络和中国农民工社会网络的社区结构探测
结果表明所提算法比Newman的迭代次数减少近50%
并且可以获得更好的模块性指标.
On the basis of analyzing the modularity and Newman detecting algorithm for network community structure
an algorithm based on prior knowledge and modularity(PKM)is put forward to detect community structure. An original community division is acquired by using the prior knowledge of the structure of social networks
such as the degree of the node
and then the communities are combined so as to get a clarified partition. Through calculation of computer simulation networks
Ucinet networks and Chinese rural-urban migrants social networks
the results indicate that the number of iterations of the proposed algorithm is reduced nearly by 50% compared to that of Newman's
and the higher modularity can be yielded.
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