基于谱顶层分割的网络社区层次抽取方法

针对网络层次中不同尺度上社区内连接密度的异构性,提出了基于谱顶层分割的网络社区层次抽取方法.首先,将网络的谱顶层分割定义为某个子网络的二分,给出了顶层分割的期望划分;然后,引入队列的思想计算社区连接密度,自顶向下逐层分解给定网络,并提出了社区层次抽取算法;最后,通过实验表明:所提出的方法比同步法和多尺度法在随机层次网络测试的性能更加优越,为社区教育和大数据行为特征识别提供了相关技术基础支持.

Extraction of Network Community Hierarchies Based on Spectrum Top-Segmentation

The network community hierarchies are defined by heterogeneous of different scales of link density in essence, it is necessary for network community to detect the dynamically changing information during hierarchies division. In view of this, a method of extraction of network community hierarchies based on spectrum top-segmentation is proposed. Firstly, the spectrum top-segmentation is defined as a dichotomy of subnetwork that no any top-level community can cross two parts, and an expected division top-level segmentation is presented. Then, the queue and link-density are introduced to decompose network, and an algorithm of network community levels extraction is presented. The simulation result shows that the performance of proposed method is better than that of synchronization and multi-scale in stochastic hierarchical networks, and the method is applicated in Email real-world network effectively.