时序网络中的社团探测及演化分析方法
发布时间：2018-07-20 11:02
【摘要】：在传统的动态社团探测方法中,由于每个时间片网络之间相互独立,无法高效地探测社团并分析社团的演化事件。针对传统方法的不足,提出一种利用时序网络的历史信息,解决上述两个问题。对于每个时间片网络,仅计算连边发生改变的节点;根据社团的定义及上一时刻的社团信息,探测当前时刻的社团并分析它们的演化事件。在人工网络上的实验结果表明,相对传统方法,该方法能够保证社团划分的质量并分析社团的演化事件,提升了探测效率。
[Abstract]:In the traditional dynamic community detection method, because each time slice network is independent of each other, it is unable to detect the community and analyze the evolution events of the community efficiently. In view of the shortcomings of traditional methods, this paper presents a method to solve the above two problems by using the historical information of time series network. For each time slice network, only the nodes whose edges have changed are calculated. According to the definition of the community and the information of the community at the last moment, the community at the current time is detected and their evolution events are analyzed. The experimental results on artificial networks show that this method can guarantee the quality of community division and analyze the evolution events of communities, and improve the detection efficiency.
【作者单位】： 电子科技大学互联网中心;电子科技大学大数据研究中心;
【基金】：国家自然科学基金项目(61433014、61673085) 中央高校基本科研基金项目(ZYGX2014Z002)
【分类号】：O157.5
本文编号：2133313
[Abstract]:In the traditional dynamic community detection method, because each time slice network is independent of each other, it is unable to detect the community and analyze the evolution events of the community efficiently. In view of the shortcomings of traditional methods, this paper presents a method to solve the above two problems by using the historical information of time series network. For each time slice network, only the nodes whose edges have changed are calculated. According to the definition of the community and the information of the community at the last moment, the community at the current time is detected and their evolution events are analyzed. The experimental results on artificial networks show that this method can guarantee the quality of community division and analyze the evolution events of communities, and improve the detection efficiency.
【作者单位】： 电子科技大学互联网中心;电子科技大学大数据研究中心;
【基金】：国家自然科学基金项目(61433014、61673085) 中央高校基本科研基金项目(ZYGX2014Z002)
【分类号】：O157.5
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