A Comprehensive Review of Core-Periphery and Community Detection Paradigms

TL;DR

This survey addresses the lack of a unified framework for core–periphery and community structures in meso-scale networks. It methodically catalogs detection methods across block models, centrality, diffusion, geometry, and probabilistic inference, and it surveys applications from social and biological to financial systems. It also contrasts CP and community paradigms, discusses their concurrent presence in hyperbolic network models, and evaluates their implications for portfolio optimization and risk management. The paper concludes with open challenges, including temporal and multilayer extensions, cross-domain generalization, and robust statistical inference, offering guidance for future research and practice in network science.

Abstract

Meso-scale structures, such as core-periphery (CP) and community structure, have attracted significant attention in modern network science. While communities are characterized by dense intra-group and sparse inter-group connections, CP structures consist of a densely interconnected core and a loosely connected periphery, where peripheral nodes are typically linked to the core. Despite growing interest, identifying CP structures remains an ill-posed problem, with no universally accepted definition or standardized detection methodology. This ambiguity has led to conceptual overlaps, inconsistent evaluation metrics and slowed methodological progress. In this review, we provide a structured overview of foundational concepts, recent advances, key challenges and comparative evaluations of CP detection approaches, along with a discussion of their interplay with community structure and applications in real-world networks.

Figures (2)

• Figure 1: Illustration of a network with (a) a CP structure and (b) an ideal CP structure
• Figure 2: A simple illustration of a network divided into communities. Nodes within the same group share denser connections than those across groups.