网络中的核心-边缘结构：一个统计解释
Core-periphery structure in networks: a statistical exposition
摘要：
许多现实世界的网络理论上具有核心-外围结构，由密集连接的核心和松散连接的外围组成。虽然这一现象已在一系列科学学科中得到广泛研究，但在统计界尚未得到足够重视。在这篇简短的文章中，我们的目标是提高对这个主题的认识，并鼓励统计人员解决这个领域中的许多开放式推理问题。为此，我们首先总结了目前的研究景观，通过审查的指标和模型，已用于定量研究的核心-外围结构。接下来，我们制定和探讨各种推理问题，在这种情况下，如估计，假设检验，和贝叶斯推断，并讨论相关的计算技术。我们还概述了核心-外围结构在一些现实世界的网络中的多学科科学影响。在整篇文章中，我们从统计学的角度对文献进行了自己的解释，目标是优先考虑统计界的贡献最有效和最重要的开放问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
