General expression for the component size distribution in infinite configuration networks
arXiv:1703.05413 · doi:10.1103/PhysRevE.95.052303
Abstract
In the infinite configuration network the links between nodes are assigned randomly with the only restriction that the degree distribution has to match a predefined function. This work presents a simple equation that gives for an arbitrary degree distribution the corresponding size distribution of connected components. This equation is suitable for fast and stable numerical computations up to the machine precision. The analytical analysis reveals that the asymptote of the component size distribution is completely defined by only a few parameters of the degree distribution: the first three moments, scale and exponent (if applicable). When the degree distribution features a heavy tail, multiple asymptotic modes are observed in the component size distribution that, in turn, may or may not feature a heavy tail.
References in corpus (5)
- Scale-free brain functional networks
- Component sizes in networks with arbitrary degree distributions
- Densification and Structural Transitions in Networks that Grow by Node Copying
- Scale-free networks with exponent one
- Emergence of the giant weak-component in directed random graphs with arbitrary degree distributions
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