# Finding multiple core-periphery pairs in networks

## TL;DR

This paper introduces a scalable algorithm to detect multiple core-periphery pairs in networks, revealing complex structures like political and airport subnetworks that single-core models cannot capture.

## Contribution

The authors propose a novel method for identifying multiple non-overlapping core-periphery groups, extending beyond traditional single-core models.

## Key findings

- Identified distinct core-periphery pairs in political blog networks.
- Detected separation between international and domestic airport subnetworks.
- Validated the algorithm on synthetic and real-world networks.

## Abstract

With a core-periphery structure of networks, core nodes are densely interconnected, peripheral nodes are connected to core nodes to different extents, and peripheral nodes are sparsely interconnected. Core-periphery structure composed of a single core and periphery has been identified for various networks. However, analogous to the observation that many empirical networks are composed of densely interconnected groups of nodes, i.e., communities, a network may be better regarded as a collection of multiple cores and peripheries. We propose a scalable algorithm to detect multiple non-overlapping groups of core-periphery structure in a network. We illustrate our algorithm using synthesised and empirical networks. For example, we find distinct core-periphery pairs with different political leanings in a network of political blogs and separation between international and domestic subnetworks of airports in some single countries in a world-wide airport network.