We derive the finite-size dependence of the clustering coefficient of scale-free random graphs generated by the configuration model with degree distribution exponent 2<γ<3. Degree heterogeneity increases the presence of triangles in the network up to levels that compare to those found in many real networks even for extremely large nets. We also find that for values of γ≈2, clustering is virtually size independent and, at the same time, becomes a de facto non-self-averaging topological property. This implies that a single-instance network is not representative of the ensemble even for very large network sizes
Complex network theory crucially depends on the assumptions made about the degree distribution, whil...
We present a generator of random networks where both the degree-dependent clustering coefficient and...
In a network cliques are fully connected subgraphs that reveal which are the tight communities prese...
We derive the finite-size dependence of the clustering coefficient of scale-free random graphs gener...
Random graphs with power-law degrees can model scale-free networks as sparse topologies with strong ...
Abstract. In this paper, we analyze the behavior of the global cluster-ing coefficient in scale free...
We investigate the presence of triangles in a class of correlated random graphs in which hidden vari...
We study a recently introduced class of scale-free networks showing a high clustering coefficient an...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
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...
Complex network theory crucially depends on the assumptions made about the degree distribution, whil...
We present a generator of random networks where both the degree-dependent clustering coefficient and...
In a network cliques are fully connected subgraphs that reveal which are the tight communities prese...
We derive the finite-size dependence of the clustering coefficient of scale-free random graphs gener...
Random graphs with power-law degrees can model scale-free networks as sparse topologies with strong ...
Abstract. In this paper, we analyze the behavior of the global cluster-ing coefficient in scale free...
We investigate the presence of triangles in a class of correlated random graphs in which hidden vari...
We study a recently introduced class of scale-free networks showing a high clustering coefficient an...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
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...
Complex network theory crucially depends on the assumptions made about the degree distribution, whil...
We present a generator of random networks where both the degree-dependent clustering coefficient and...
In a network cliques are fully connected subgraphs that reveal which are the tight communities prese...