Efficient stepwise detection of communities in temporal networks
Efficient stepwise detection of communities in temporal networks
复制标题
时间网络中社区的高效逐步检测
DOI：
10.1016/j.physa.2016.11.019
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Li Wenjun
中科院分区：
文献类型：
--
作者：
He Jialin;Chen Duanbing;Sun Chongjing;Fu Yan;Li Wenjun
在时间网络中，动态社区检测由两个独立的阶段组成：（i）在每个时间步的社区检测;（ii）跨时间步的社区匹配。在传统的方法中，跨时间步的社区匹配是基于节点的，这是耗时的。本文提出了一种利用历史社区信息来检测动态社区的简单方法。将前一个时间步的社区划分为几个模块后，我们不仅可以使用这些模块来检测当前时间步的社区，而且可以跨时间步映射社区。在合成网络和真实的网络上的实验结果表明，该方法不仅保持了社区的质量，而且显著提高了社区匹配的效率。
In temporal networks, dynamic community detection is composed of two separate stages: (i) community detection at each time step; (ii) community matching across time steps. In the traditional methods, the community matching across time steps is based on nodes, which is time consuming. In this paper, we suggest a simple method which takes advantage of historic community information to detect dynamic communities. After dividing each community at previous time step into a few modules, we cannot only use these modules to detect communities at current time step but also map communities across time steps. Results on synthetic and real networks demonstrate that our method cannot only maintain the quality of communities but also improve the efficiency of community matching significantly.