Uncovering the hidden core-periphery structure in hyperbolic networks
This study extends network science by revealing core-periphery insights applicable to domains like transportation and information systems, though it is incremental as it applies an existing method to known models.
The paper investigates the presence of core-periphery structures in hyperbolic network models, finding that these structures can be pronounced under certain conditions, as indicated by core-periphery centralization values.
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.