Add to ORKGWe introduce, and analyze, three measures for degree-degree dependencies, also called degree assortativity, in directed random graphs, based on Spearman’s rho and Kendall’s tau. We proof statistical consistency of these measures in general random graphs and show that the directed Configuration Model can serve as a null model for our degree-degree dependency measures. Based on these results we argue that the measures we introduce should be preferred over Pearson’s correlation coefficients, when studying degree-degree dependencies, since the latter has several issues in the case of large networks with scale-free degree distributions
We present a generator of random networks where both the degree-dependent clustering coefficient and...
In complex networks the degrees of adjacent nodes may often appear dependent -- which presents a mod...
Complex networks have been applied to model numerous interactive nonlinear systems in the real world...
We introduce, and analyze, three measures for degree-degree dependencies, also called degree assorta...
Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, social,...
In network theory, Pearson's correlation coefficients are most commonly used to measure the degree a...
Scale-free networks, in which the distribution of the degrees obeys a power-law, are ubiquitous in t...
Barabási and Albert [1] suggested modeling scale-free networks by the following random graph process...
We study the average nearest neighbor degree $a(k)$ of vertices with degree $k$. In many real-world ...
Analysis of degree-degree dependencies in complex networks, and their impact on processes on network...
<div><p>Scale-free networks, in which the distribution of the degrees obeys a power-law, are ubiquit...
Complex network theory crucially depends on the assumptions made about the degree distribution, whil...
Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, and soc...
Our world is filled with complex systems, ranging from technological systems such as the Internet an...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
We present a generator of random networks where both the degree-dependent clustering coefficient and...
In complex networks the degrees of adjacent nodes may often appear dependent -- which presents a mod...
Complex networks have been applied to model numerous interactive nonlinear systems in the real world...
We introduce, and analyze, three measures for degree-degree dependencies, also called degree assorta...
Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, social,...
In network theory, Pearson's correlation coefficients are most commonly used to measure the degree a...
Scale-free networks, in which the distribution of the degrees obeys a power-law, are ubiquitous in t...
Barabási and Albert [1] suggested modeling scale-free networks by the following random graph process...
We study the average nearest neighbor degree $a(k)$ of vertices with degree $k$. In many real-world ...
Analysis of degree-degree dependencies in complex networks, and their impact on processes on network...
<div><p>Scale-free networks, in which the distribution of the degrees obeys a power-law, are ubiquit...
Complex network theory crucially depends on the assumptions made about the degree distribution, whil...
Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, and soc...
Our world is filled with complex systems, ranging from technological systems such as the Internet an...
The average nearest neighbor degree (ANND) of a node of degree k is widely used to measure dependenc...
We present a generator of random networks where both the degree-dependent clustering coefficient and...
In complex networks the degrees of adjacent nodes may often appear dependent -- which presents a mod...
Complex networks have been applied to model numerous interactive nonlinear systems in the real world...