Multilayer networks provide a powerful framework for modeling complex systems that capture different types of interactions between the same set of entities across multiple layers. Core-periphery detection involves partitioning the nodes of a network into core nodes, which are highly connected across the network, and peripheral nodes, which are densely connected to the core but sparsely connected among themselves. In this paper, we propose a new model of core-periphery in multilayer network and a nonlinear spectral method that simultaneously detects the corresponding core and periphery structures of both nodes and layers in weighted and directed multilayer networks. Our method reveals novel structural insights in three empirical multilayer networks from distinct application areas: the citation network of complex network scientists, the European airlines transport network, and the world trade network.

翻译：多层网络为复杂系统建模提供了一个强大的框架，能够捕捉同一组实体在不同层之间的多种交互类型。核心-边缘检测涉及将网络节点划分为核心节点和边缘节点：核心节点在整个网络中高度连接，而边缘节点与核心节点紧密相连但彼此间连接稀疏。本文提出了一种多层网络中的核心-边缘新模型，以及一种非线性谱方法，该方法能够同时检测加权有向多层网络中节点和层所对应的核心与边缘结构。我们的方法在三个来自不同应用领域的实证多层网络中揭示了新颖的结构洞见：复杂网络科学家的引文网络、欧洲航空运输网络以及世界贸易网络。