Many real-world networks are theorized to have core-periphery structure consisting of a densely-connected core and a loosely-connected periphery. While this phenomenon has been extensively studied in a range of scientific disciplines, it has not received sufficient attention in the statistics community. In this expository article, our goal is to raise awareness about this topic and encourage statisticians to address the many open inference problems in this area. To this end, we first summarize the current research landscape by reviewing the metrics and models that have been used for quantitative studies on core-periphery structure. Next, we formulate and explore various inferential problems in this context, such as estimation, hypothesis testing, and Bayesian inference, and discuss related computational techniques. We also outline the multidisciplinary scientific impact of core-periphery structure in a number of real-world networks. Throughout the article, we provide our own interpretation of the literature from a statistical perspective, with the goal of prioritizing open problems where contribution from the statistics community will be most effective and important.

翻译：许多真实网络理论上具有核心-边缘结构，该结构由紧密连接的核心和松散连接的边缘组成。尽管这一现象已在多个科学领域中得到广泛研究，但在统计学界却尚未获得足够关注。本文旨在通过综述性阐述提升学界对此课题的认知，并鼓励统计学家解决该领域中诸多待解决的推断问题。为此，我们首先通过回顾用于核心-边缘结构定量研究的度量标准与模型，总结当前研究现状。其次，我们系统阐述并探究此背景下的各类推断问题——包括参数估计、假设检验与贝叶斯推断——同时讨论相关计算方法。此外，我们还概述了核心-边缘结构在若干真实网络中的跨学科科学影响。全文从统计学视角对现有文献进行解读，旨在优先关注那些统计学界的贡献将最为有效且重要的开放性问题。