🤖 AI Summary
Core-periphery structure lacks a unified definition and standardized detection methodology, leading to conceptual ambiguity and inconsistent evaluation—hindering both theoretical advancement and practical application. This paper addresses this gap through a systematic literature review and methodological comparison, integrating graph-theoretic modeling, clustering algorithms, and structural evaluation metrics to classify, empirically benchmark, and delineate the boundaries of mainstream core-periphery detection methods. It clarifies their distinctions from and relationships with community structure, along with contextual applicability conditions. The study establishes a comprehensive theoretical framework encompassing conceptual foundations, a taxonomy of methods, and principled evaluation criteria; identifies key open challenges; and proposes a standardized definition and a reproducible, metric-driven assessment protocol. These contributions provide a systematic foundation for algorithm design, cross-method comparison, and empirical analysis of real-world networks.
📝 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.