Gallagher Ryan J, Young Jean-Gabriel, Welles Brooke Foucault
Network Science Institute, Northeastern University, Boston, MA 02115, USA.
Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA.
Sci Adv. 2021 Mar 17;7(12). doi: 10.1126/sciadv.abc9800. Print 2021 Mar.
Core-periphery structure, the arrangement of a network into a dense core and sparse periphery, is a versatile descriptor of various social, biological, and technological networks. In practice, different core-periphery algorithms are often applied interchangeably despite the fact that they can yield inconsistent descriptions of core-periphery structure. For example, two of the most widely used algorithms, the -cores decomposition and the classic two-block model of Borgatti and Everett, extract fundamentally different structures: The latter partitions a network into a binary hub-and-spoke layout, while the former divides it into a layered hierarchy. We introduce a core-periphery typology to clarify these differences, along with Bayesian stochastic block modeling techniques to classify networks in accordance with this typology. Empirically, we find a rich diversity of core-periphery structure among networks. Through a detailed case study, we demonstrate the importance of acknowledging this diversity and situating networks within the core-periphery typology when conducting domain-specific analyses.
核心-边缘结构，即将网络排列成密集的核心和稀疏的边缘，是对各种社会、生物和技术网络的一种通用描述。实际上，尽管不同的核心-边缘算法可能会对核心-边缘结构产生不一致的描述，但它们常常被交替使用。例如，两种最广泛使用的算法，k-核分解和Borgatti与Everett的经典两模块模型，提取出的结构根本不同：后者将网络划分为二元中心辐射布局，而前者将其划分为分层层次结构。我们引入一种核心-边缘类型学来阐明这些差异，并引入贝叶斯随机块建模技术，以便根据这种类型学对网络进行分类。从经验上看，我们发现网络之间的核心-边缘结构具有丰富的多样性。通过详细的案例研究，我们证明了在进行特定领域分析时，认识到这种多样性并将网络置于核心-边缘类型学中的重要性。
