Detection of Core-Periphery Structure in Networks Using Spectral Methods and Geodesic Paths

TL;DR

This paper presents three novel spectral and path-based methods for efficiently detecting core-periphery structures in networks, validated on synthetic and real-world data.

Contribution

The paper introduces new computationally efficient algorithms leveraging spectral methods and geodesic paths for core-periphery detection in networks.

Findings

Methods accurately identify core-periphery structures

Algorithms perform well on synthetic and real networks

Provides classification and goodness-of-fit criteria

Abstract

We introduce several novel and computationally efficient methods for detecting "core--periphery structure" in networks. Core--periphery structure is a type of mesoscale structure that includes densely-connected core vertices and sparsely-connected peripheral vertices. Core vertices tend to be well-connected both among themselves and to peripheral vertices, which tend not to be well-connected to other vertices. Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that a vertex is a core vertex. Our second method is based on a low-rank approximation of a network's adjacency matrix, which can often be expressed as a tensor-product matrix. Our third approach uses the bottom eigenvector of the random-walk Laplacian to infer a coreness score and a classification into core and peripheral vertices. We also design an objective function to (1) help classify vertices into core or peripheral vertices and (2) provide a goodness-of-fit criterion for classifications into core versus peripheral vertices. To examine the performance of our methods, we apply our algorithms to both synthetically-generated networks and a variety of networks constructed from real-world data sets.