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International Journal of Next-Generation Engineering and Technology

Open Access Peer Review International
Open Access

A Principal Component Analysis Framework for Characterizing Core-Periphery Structures through Neighborhood-Based Bridge Node Centrality

DOI https://doi.org/10.55640/ijnget-v02i09-01
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Dr. Miguel A. Rodríguez 1 ,
4 Department of Applied Mathematics and Computer Science, Technical University of Madrid (UPM), Spain

Abstract

Background: The core-periphery (C-P) structure is a fundamental feature of complex networks, yet its characterization remains a significant challenge. Existing methods often impose a discrete partition on the network, classifying nodes as either core or periphery, which oversimplifies the diverse and continuous roles nodes can play.

Methods: We propose a novel framework for a more nuanced C-P characterization. First, we introduce a "Neighborhood-based Bridge Node Centrality" metric, designed to quantify the extent to which a node connects its local neighborhood to the wider network. We then apply Principal Component Analysis (PCA) to a node-feature matrix derived from this metric. The resulting principal components provide a low-dimensional embedding where nodes are positioned based on their topological roles. A clustering algorithm is then used on this embedding to identify core, periphery, and intermediate structures.

Results: On synthetic networks with known C-P structures, our framework demonstrates high accuracy. When applied to real-world networks, including a jazz musician collaboration network, it reveals a continuous spectrum of "coreness" and effectively identifies bridge nodes that are critical for network cohesion. A comparative analysis shows our method provides a richer characterization than traditional approaches based on discrete optimization and spectral methods.

Conclusion: The proposed PCA framework offers a flexible, interpretable, and powerful tool for analyzing core-periphery structures. By moving beyond a binary classification, it provides deeper insights into the complex topology of networks, with significant implications for understanding dynamics like influence spreading and system resilience.

Keywords

Network Science, Core-Periphery Structure, Principal Component Analysis (PCA)

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