揭示双曲网络中的隐藏核心-边缘结构
Uncovering the hidden core-periphery structure in hyperbolic networks
AI总结：
本文系统研究了PSO和S1/H2双曲网络模型中的核心-边缘结构，采用基于随机游走马尔可夫链的方法进行量化分析，并通过统计检验验证其显著性，为提升交通与信息系统网络性能提供新见解。
摘要：
双曲网络模型展现出非常基础且本质的特征，如小世界性、无标度性、高聚类系数和社区结构。在本文中，我们全面探讨了双曲网络模型中一个重要特征——核心-边缘结构的存在性，该结构常为现实世界网络所展现。我们聚焦于著名的双曲模型，如流行度-相似性优化模型（PSO）和S1/H2模型，并使用一种基于标准随机游走马尔可夫链模型的成熟方法研究了核心-边缘结构。观测到的核心-边缘集中化值表明，在某些条件下核心-边缘结构可以非常显著。我们还通过在网络几何中对观测到的核心-边缘结构的显著性进行统计检验来验证我们的发现。本研究拓展了网络科学，并揭示了适用于交通和信息系统等多个领域的核心-边缘见解，从而提升网络性能和韧性。
英文摘要：
The hyperbolic network models exhibit very fundamental and essential features, like small-worldness, scale-freeness, high-clustering coefficient, and community structure. In this paper, we comprehensively explore the presence of an important feature, the core-periphery structure, in the hyperbolic network models, which is often exhibited by real-world networks. We focused on well-known hyperbolic models such as popularity-similarity optimization model (PSO) and S1/H2 models and studied core-periphery structures using a well-established method that is based on standard random walk Markov chain model. The observed core-periphery centralization values indicate that the core-periphery structure can be very pronounced under certain conditions. We also validate our findings by statistically testing for the significance of the observed core-periphery structure in the network geometry. This study extends network science and reveals core-periphery insights applicable to various domains, enhancing network performance and resiliency in transportation and information systems.
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。
