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• W3112055712 startingPage "174" @default.
• W3112055712 abstract "Abstract Random graph models are a recurring tool‐of‐the‐trade for studying network structural properties and benchmarking community detection and other network algorithms. Moreover, they serve as test‐bed generators for studying diffusion and routing processes on networks. In this paper, we illustrate how to generate large random graphs having a power‐law degree distribution using the Chung–Lu model. In particular, we are concerned with the fulfillment of a fundamental hypothesis that must be placed on the model parameters, without which the generated graphs lose all the theoretical properties of the model, notably, the controllability of the expected node degrees and the absence of correlations between the degrees of two nodes joined by an edge. We provide explicit formulas for the model parameters to generate random graphs that have several desirable properties, including a power‐law degree distribution with any exponent larger than 2, a prescribed asymptotic behavior of the largest and average expected degrees, and the presence of a giant component." @default.
• W3112055712 created "2020-12-21" @default.
• W3112055712 creator A5009056146 @default.
• W3112055712 creator A5043752696 @default.
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• W3112055712 date "2020-12-08" @default.
• W3112055712 modified "2023-10-16" @default.
• W3112055712 title "Generating large scale‐free networks with the Chung–Lu random graph model" @default.
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• W3112055712 doi "https://doi.org/10.1002/net.22012" @default.
• W3112055712 hasPublicationYear "2020" @default.
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