We study the average nearest neighbor degree $a(k)$ of vertices with degree $k$. In many real-world networks with power-law degree distribution $a(k)$ falls off in $k$, a property ascribed to the constraint that any two vertices are connected by at most one edge. We show that $a(k)$ indeed decays in $k$ in three simple random graph null models with power-law degrees: the erased configuration model, the rank-1 inhomogeneous random graph and the hyperbolic random graph. We consider the large-network limit when the number of nodes $n$ tends to infinity. We find for all three null models that $a(k)$ starts to decay beyond $n^{(\tau-2)/(\tau-1)}$ and then settles on a power law $a(k)\sim k^{\tau-3}$, with $\tau$ the degree exponent
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
We study the average nearest neighbor degree $a(k)$ of vertices with degree $k$. In many real-world ...
We study the average nearest neighbor degree $a(k)$ of vertices with degree $k$. In many real-world ...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
The configuration model generates random graphs with any given degree distribution, and thus serves ...
The configuration model generates random graphs with any given degree distribution, and thus serves ...
The configuration model generates random graphs with any given degree distribution, and thus serves ...
The configuration model generates random graphs with any given degree distribution, and thus serves ...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
We study the average nearest neighbor degree $a(k)$ of vertices with degree $k$. In many real-world ...
We study the average nearest neighbor degree $a(k)$ of vertices with degree $k$. In many real-world ...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
The configuration model generates random graphs with any given degree distribution, and thus serves ...
The configuration model generates random graphs with any given degree distribution, and thus serves ...
The configuration model generates random graphs with any given degree distribution, and thus serves ...
The configuration model generates random graphs with any given degree distribution, and thus serves ...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...