2016/12/31 by Bing-Bing Xiang, Zhong-Kui Bao, Chuang Ma +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #Clique percolation method #Community structure #Complex Network Analysis Techniques #Complex network #Data structure #Network structure #cs.SI #physics.soc-ph
paper · pdf · doi:10.1063/1.4990734
published as Chaos, 28,013122, (2018) · 10 figures. Accepted by Chaos
openalex created_date 2017/02/10 · openalex publication_date 2018/01/01 · arxiv created 2018/01/12 · arxiv updated 2018/01/25 · openalex updated_date 2026/08/05
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.