Uncovering the hidden core-periphery structure in hyperbolic networks

• URL: http://arxiv.org/abs/2406.19953v1
• Date: Fri, 28 Jun 2024 14:39:21 GMT
• Title: Uncovering the hidden core-periphery structure in hyperbolic networks
• Authors: Imran Ansari, Pawanesh Yadav, Niteesh Sahni,
• Abstract summary: hyperbolic network models exhibit fundamental and essential features, like small-worldness, scale-freeness, high-clustering coefficient, and community structure. In this paper, we explore the presence of an important feature, the core-periphery structure, in the hyperbolic network models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The hyperbolic network models exhibit very fundamental and essential features, like small-worldness, scale-freeness, high-clustering coefficient, and community structure. In this paper, we comprehensively explore the presence of an important feature, the core-periphery structure, in the hyperbolic network models, which is often exhibited by real-world networks. We focused on well-known hyperbolic models such as popularity-similarity optimization model (PSO) and S1/H2 models and studied core-periphery structures using a well-established method that is based on standard random walk Markov chain model. The observed core-periphery centralization values indicate that the core-periphery structure can be very pronounced under certain conditions. We also validate our findings by statistically testing for the significance of the observed core-periphery structure in the network geometry. This study extends network science and reveals core-periphery insights applicable to various domains, enhancing network performance and resiliency in transportation and information systems.

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