A Unified Method of Detecting Core-Periphery Structure and Community Structure in Networks

TL;DR

This paper introduces a unified framework for detecting both core-periphery and community structures in complex networks, capable of identifying multiple and overlapping structures at various scales, validated on synthetic and real data.

Contribution

The paper presents a novel unified method that simultaneously detects core-periphery and community structures, including overlapping and multi-scale features, which were previously studied separately.

Findings

Effective detection of multiple core-periphery pairs

Identification of overlapping nodes in communities

Validated performance on synthetic and real networks

Abstract

Core-periphery structure and community structure are two typical meso-scale structures in complex networks. Though the community detection has been extensively investigated from different perspectives, the definition and the detection of core-periphery structure have not received much attention. Furthermore, the detection problems of the core-periphery and community structure were separately investigated. In this paper, we develop a unified framework to simultaneously detect core-periphery structure and community structure in complex networks. Moreover, there are several extra advantages of our algorithm: our method can detect not only single but also multiple pairs of core-periphery structures; the overlapping nodes belonging to different communities can be identified; different scales of core-periphery structures can be detected by adjusting the size of core. The good performance of the method has been validated on synthetic and real complex networks. So we provide a basic framework to detect the two typical meso-scale structures: core-periphery structure and community structure.