论文标题
网络中核心外围结构的明确类型
A Clarified Typology of Core-Periphery Structure in Networks
论文作者
论文摘要
核心外围结构是将网络排列成密集的核心和稀疏外围的结构，是各种社会，生物学和技术网络的多功能描述符。在实践中，尽管它们可以产生不一致的核心围结构描述，但通常会互换地应用不同的外围算法。例如，两种最广泛使用的算法，K核分解和经典的Borgatti和Everett的经典两块模型，从根本上提取了根本不同的结构：后者将网络分为二进制枢纽和辐条布局，而前者则将其分为分层的层次。我们引入了一种核心外围类型学，以阐明这些差异，以及贝叶斯随机块建模技术，以根据此类型学对网络进行分类。从经验上讲，我们发现网络之间的核心外围结构各种各样。通过详细的案例研究，我们证明了在进行特定于领域的分析时，在核心 - 周期类型中承认这种多样性和位置网络的重要性。
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 k-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.
