Generation of uncorrelated random scale-free networks.

By Michele Catanzaro, Marián Boguñá, Romualdo Pastor-Satorras. Physical review. E, Statistical, nonlinear, and soft matter physics, 2004

606 citations recorded by the source corpus.

Uncorrelated random scale-free networks are useful null models to check the accuracy and the analytical solutions of dynamical processes defined on complex networks. We propose and analyze a model capable of generating random uncorrelated scale-free networks with no multiple and self-connections. The model is based on the classical configuration model, with an additional restriction on the maximum possible degree of the vertices. We check numerically that the proposed model indeed generates scale-free networks with no two- and three-vertex correlations, as measured by the average degree of the nearest neighbors and the clustering coefficient of the vertices of degree k , respectively.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6df8c648dfe16976aa7fe8d9fe8615772b7e110e

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Citations in the corpus

Evolutionary dynamics on any population structure (2016, methodology): “b, Scale-free graphs with tail exponent 2 < γ ≤ 3 were generated according to the uncorrelated configuration model [42].”
Modeling human mobility responses to the large-scale spreading of infectious diseases (2011, methodology): “Synthetic scale free networks were generated following the recipe in [3].”
Reaction–diffusion processes and metapopulation models in heterogeneous networks (2007, methodology): “Monte Carlo simulations The uncorrelated networks considered have been generated with the uncorrelated configuration mode”
Structure and Dynamics of Networks (2009, background): “The choice of the maximal degree kmax turns out to be quite peculiar in the generation of a scale-free network [Catanzaro et al., 2005b].”
Statistical physics of social dynamics (2007, background): “In order to account for degree heterogeneity, other constructions have been proposed for random graphs with arbitrary degree distributions (Aiello and Lu, 2001; Catanzaro et al., 2005; Goh et al., 2001; Molloy and Reed, 1995, 1998).”