Core-periphery detection in multilayer networks

TL;DR

This paper introduces a novel nonlinear spectral method for detecting core-periphery structures in multilayer networks, revealing new insights across diverse real-world systems.

Contribution

It proposes a new model and a nonlinear spectral approach for joint core-periphery detection in weighted, directed multilayer networks, advancing structural analysis.

Findings

Revealed core-periphery structures in empirical networks

Uncovered novel structural insights in three application domains

Demonstrated effectiveness of the method on real-world data

Abstract

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.