《计算机应用研究》|Application Research of Computers

动态加权网络中的演化社区发现算法研究

Research on evolutionary community discovery algorithm in dynamic weighted networks

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作者 张高祯,张贤坤,苏静,刘渊博
机构 天津科技大学 计算机科学与信息工程学院,天津 300457
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文章编号 1001-3695(2019)04-008-0992-05
DOI 10.19734/j.issn.1001-3695.2017.10.0966
摘要 在动态网络中发现社区结构是一个非常复杂而有意义的过程,可以更好地观察和分析网络的演化情况。针对动态加权网络中的社区发现问题,提出了一种结合历史网络社区结构的算法,叫做动态加权网络中的演化社区发现算法(ECDA)。该算法分为两步:a)结合历史社区和网络结构信息,计算当前时间跳的输入矩阵;b)通过该输入矩阵计算得到结合历史时间跳信息的社区划分结果。该算法有以下优点:a)可以自动发现动态加权网络中每个时间跳的社区结构;b)对网络结构的变化和社区结构的变化具有较高的敏锐性。在人工数据集和真实数据集中进行了实验,实验结果证明该算法可以有效地发现动态加权网络中的社区结构,与其他算法相比具有较好的竞争力。
关键词 动态网络; 加权网络; 社区发现; 模块度
基金项目 国家自然科学基金资助项目(61702367)
本文URL http://www.arocmag.com/article/01-2019-04-008.html
英文标题 Research on evolutionary community discovery algorithm in dynamic weighted networks
作者英文名 Zhang Gaozhen, Zhang Xiankun, Su Jing, Liu Yuanbo
机构英文名 School of Computer Science & Information Engineering,Tianjin University of Science & Technology,Tianjin 300457,China
英文摘要 In dynamic networks, detecting community structure is a very complex and meaningful process, which can better observe and analyze the evolution of the networks. For the community detection problem in dynamic weighted networks, this paper proposed an algorithm combining the community structure of the historical networks, called the evolutionary community discovery algorithm(ECDA) in dynamic weighted networks. The algorithm was divided into two steps: a) calculated the input matrix of the current timestep by combining the information of historical communities and network structure; b) and then calculated the result of community detection combining the historical timestep information through the input matrix. The algorithm has the following advantages: a) it can automatically discover the community structure of each timestep in the dynamic weighted network; b) the algorithm has a high sensitivity to the changes of network structure and the changes of community structure. And the experimental results show that the proposed algorithm can effectively detect the community structure in dynamic weighted networks, and it is quite competitive with other algorithms.
英文关键词 dynamic networks; weighted networks; community detection; modularity
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收稿日期 2017/10/31
修回日期 2017/12/12
页码 992-996,1005
中图分类号 TP393
文献标志码 A