Core-periphery Detection Based on Masked Bayesian Non-negative Matrix Factorization

Abstract

Core-periphery structure is an essential mesoscale feature in complex networks. Previous researches mostly focus on discriminative approaches while in this work, we propose a generative model called masked Bayesian non-negative matrix factorization. We build the model using two pair affiliation matrices to indicate core-periphery pair associations and using a mask matrix to highlight connections to core nodes. We propose an approach to infer the model parameters, and prove the convergence of variables with our approach. Besides the abilities as traditional approaches, it is able to identify core scores with overlapping core-periphery pairs. We verify the effectiveness of our method using randomly generated networks and real-world networks. Experimental results demonstrate that the proposed method outperforms traditional approaches.

arXiv comment12 pages, 11 figures. IEEE Transactions on Computational Social Systems(TCSS), 2024, early access

References in corpus (14)

• Community structure in social and biological networks
• Finding and evaluating community structure in networks
• The Product Space Conditions the Development of Nations
• Graph Evolution: Densification and Shrinking Diameters
• Hierarchical Block Structures and High-resolution Model Selection in Large Networks
• Normalized Mutual Information to evaluate overlapping community finding algorithms
• Identification of core-periphery structure in networks
• Emergence of core-peripheries in networks
• Detection of Core-Periphery Structure in Networks Using Spectral Methods and Geodesic Paths
• Finding multiple core-periphery pairs in networks
• Density-based and transport-based core-periphery structures in networks
• A Unified Method of Detecting Core-Periphery Structure and Community Structure in Networks
• Random Spatial Network Models with Core-Periphery Structure
• Detection of core-periphery structure in networks by 3-tuple motifs