  Scale-free networks arise from power-law degree distributions. Due to the
finite size of real-world networks, the power law inevitably has a cutoff at
some maximum degree $\Delta$. We investigate the relative size of the giant
component $S$ in the large-network limit. We show that $S$ as a function of
$\Delta$ increases fast when $\Delta$ is just large enough for the giant
component to exist, but increases ever more slowly when $\Delta$ increases
further. This makes that while the degree distribution converges to a pure
power law when $\Delta\to\infty$, $S$ approaches its limiting value at a slow
pace. The convergence rate also depends on the power-law exponent $\tau$ of the
degree distribution. The worst rate of convergence is found to be for the case
$\tau\approx2$, which concerns many of the real-world networks reported in the
literature.

Physics and Society;Social and Information Networks;Data Analysis, Statistics and Probability