Core-Periphery Structure in Directed Networks

TL;DR

This paper introduces a novel approach to identify core-periphery structures in directed networks, providing new insights and methods that outperform existing techniques on both simulated and real-world datasets.

Contribution

It generalizes core-periphery detection to directed networks and proposes four methods with different trade-offs, validated on benchmarks and empirical data.

Findings

Proposed methods outperform standard techniques on simulated data.

Directed core-periphery structures reveal new insights in empirical networks.

Methods are computationally efficient and adaptable to various datasets.

Abstract

While studies of meso-scale structures in networks often focus on community structure, core--periphery structures can reveal new insights. This structure typically consists of a well-connected core and a periphery that is well connected to the core but sparsely connected internally. Most studies of core--periphery structure focus on undirected networks. We propose a generalisation of core-periphery structure to directed networks. Our approach yields a family of core-periphery block model formulations in which core and periphery sets are edge-direction dependent. We mainly focus on a particular core--periphery structure consisting of two core sets and two periphery sets which we motivate empirically. To detect this directed core-periphery structure we propose four different methods, with different trade-offs between computational complexity and accuracy. We assess these methods on three benchmarks and compare to four standard methods. On simulated data, the proposed methods match or outperform the standard methods. Applying our methods to three empirical networks -- a political blogs networks, a faculty hiring network, and a trade network -- illustrates that this directed core--periphery structure can offer novel insights about the underlying dataset.