Sparse Networks with Core-Periphery Structure

TL;DR

This paper introduces a statistical model for sparse graphs with core-periphery structure, enabling detection and simulation of such structures in both simulated and real-world networks.

Contribution

It defines a precise notion of core-periphery structure based on subgraph sparsity and provides methods for simulation and posterior inference.

Findings

Model can detect core-periphery structure in networks

Provides a way to simulate graphs with core-periphery properties

Demonstrates effectiveness on real-world network data

Abstract

We propose a statistical model for graphs with a core-periphery structure. To do this we define a precise notion of what it means for a graph to have this structure, based on the sparsity properties of the subgraphs of core and periphery nodes. We present a class of sparse graphs with such properties, and provide methods to simulate from this class, and to perform posterior inference. We demonstrate that our model can detect core-periphery structure in simulated and real-world networks.