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 Δ. We investigate the relative size of the giant component S in the large-network limit. We show that S as a function of Δ increases fast when Δ is just large enough for the giant component to exist, but increases ever more slowly when Δ increases further. This gives that while the degree distribution converges to a pure power law when Δ → ∞, S approaches its limiting value at a slow pace. The convergence rate also depends on the power-law exponent τ of the degree distribution. The worst rate of convergence is found to be for the case , which concerns many of the real-worl...
We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test w...
<p>The network size is 900. The parameter α is 1.0, 0.7, 0.3 and 0.0 respectively. α = 1 corresponds...
We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test w...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
<p>The top row again shows histograms of the momentary degree distribution for networks at mean degr...
We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test w...
<p>The network size is 900. The parameter α is 1.0, 0.7, 0.3 and 0.0 respectively. α = 1 corresponds...
We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test w...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
Scale-free networks arise from power-law degree distributions. Due to the finite size of real-world ...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
In the infinite configuration network the links between nodes are assigned randomly with the only re...
<p>The top row again shows histograms of the momentary degree distribution for networks at mean degr...
We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test w...
<p>The network size is 900. The parameter α is 1.0, 0.7, 0.3 and 0.0 respectively. α = 1 corresponds...
We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test w...