Xiang Bing-Bing, Bao Zhong-Kui, Ma Chuang, Zhang Xingyi, Chen Han-Shuang, Zhang Hai-Feng
School of Mathematical Science, Anhui University, Hefei 230601, People's Republic of China.
Institute of Bio-inspired Intelligence and Mining Knowledge, School of Computer Science and Technology, Anhui University, Hefei 230601, China.
Chaos. 2018 Jan;28(1):013122. doi: 10.1063/1.4990734.
The core-periphery structure and the community structure are two typical meso-scale structures in complex networks. Although community detection has been extensively investigated from different perspectives, the definition and the detection of the core-periphery structure have not received much attention. Furthermore, the detection problems of the core-periphery and community structure were separately investigated. In this paper, we develop a unified framework to simultaneously detect the core-periphery structure and community structure in complex networks. Moreover, there are several extra advantages of our algorithm: our method can detect not only single but also multiple pairs of core-periphery structures; the overlapping nodes belonging to different communities can be identified; different scales of core-periphery structures can be detected by adjusting the size of the core. The good performance of the method has been validated on synthetic and real complex networks. So, we provide a basic framework to detect the two typical meso-scale structures: the core-periphery structure and the community structure.
核心-边缘结构和社区结构是复杂网络中两种典型的中观尺度结构。尽管从不同角度对社区检测进行了广泛研究，但核心-边缘结构的定义和检测尚未受到太多关注。此外，核心-边缘结构和社区结构的检测问题是分别研究的。在本文中，我们开发了一个统一框架，用于同时检测复杂网络中的核心-边缘结构和社区结构。此外，我们的算法还有几个额外的优点：我们的方法不仅可以检测单个核心-边缘结构对，还可以检测多个核心-边缘结构对；可以识别属于不同社区的重叠节点；通过调整核心大小可以检测不同尺度的核心-边缘结构。该方法在合成和真实复杂网络上的良好性能得到了验证。因此，我们提供了一个检测两种典型中观尺度结构的基本框架：核心-边缘结构和社区结构。
