摘要
针对复杂网络社区跟踪中存在忽略演化时域因素以及忽略网络成员演化差异性不足等问题,提出一种社区跟踪方法.对相似函数添加时域信息,并考虑网络演化的平滑性与节点间的差异性,提取网络中的活跃节点进行社区跟踪.实验表明,该算法在DBLP数据集上能比其他社区跟踪算法更好地发现社区演化过程,且找到的社区信息相似度较高.
The research of complex networks is mainly aimed at the complex systems, and it is a general method for dealing with various problems in complex systems. In general, complex network community tracking neglects evolutionary time domain factors and differences in the evolution of network members. This paper proposes a community tracking method that includes time domain information in the similarity function, and extracts active nodes in the network by taking into account smoothness of network evolution and differences between nodes. Experiments show that the proposed algorithm finds the community evolution process better than those based on DBLP data sets. It can Mso discover community similarity effectively.
作者
杨绍文
闫光辉
李雷
张海涛
YANG Shao-wen YAN Guang-hui LI Lei ZHANG Hai-tao(Electronic Information College, Lanzhou Jiaotong University, Lanzhou 730070, Chin)
出处
《应用科学学报》
CSCD
北大核心
2017年第5期602-611,共10页
Journal of Applied Sciences
基金
国家自然科学基金(No.61163010)
兰州市科技计划项目基金(No.2014-1-171)
金川公司预研基金(No.JCYY2013012)资助
关键词
社会网络分析
时域网络
社区演化
社区跟踪
活跃节点
social network analysis, temporary networks, community evolution, commu- nity track, activity point
作者简介
通信作者：闫光辉，教授，研究方向：复杂网络，E-mail：yangh9805@qq．com
