Add to ORKGIn this paper, we propose a simple rule that generates scale-free small-world networks with tunable assortative coefficient. These networks are constructed by two-stage adding process for each new node. The model can reproduce scale-free degree distributions and small-world effect. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$ by adjusting the parameter: the preferential exponent $\beta$. The model can unify the characterization of both assortative and disassortative networks
In this paper we introduce a model of spatial network growth in which nodes are placed at randomly s...
We show how scale-free degree distributions can emerge naturally from growing networks by using rand...
We propose a novel mechanism to generate a family of deterministic small-world and scale-free networ...
Uncorrelated scale-free networks are necessarily small world (and, in fact, smaller than small world...
permits unrestricted use, distribution, and reproduction in any medium, provided the original work i...
It has been shown that both humanly constructed and natural networks are often characterized by smal...
In this paper, we present a simple model of scale-free networks that incorporates both preferential ...
Abstract. We show that not only preferential attachment but also preferential depletion leads to sca...
We show that not only preferential attachment but also preferential depletion leads to scale-free ne...
Scale-free networks are characterized by a degree distribution with power-law behavior and have been...
In order to explore further the underlying mechanism of scale-free networks, we study stochastic sec...
Real-world networks process structured connections since they have non-trivial vertex degree correla...
We propose a simple mechanism for generating scale-free networks with degree exponent γ = 3, where t...
Real-world networks process structured connections since they have non-trivial vertex degree correla...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
In this paper we introduce a model of spatial network growth in which nodes are placed at randomly s...
We show how scale-free degree distributions can emerge naturally from growing networks by using rand...
We propose a novel mechanism to generate a family of deterministic small-world and scale-free networ...
Uncorrelated scale-free networks are necessarily small world (and, in fact, smaller than small world...
permits unrestricted use, distribution, and reproduction in any medium, provided the original work i...
It has been shown that both humanly constructed and natural networks are often characterized by smal...
In this paper, we present a simple model of scale-free networks that incorporates both preferential ...
Abstract. We show that not only preferential attachment but also preferential depletion leads to sca...
We show that not only preferential attachment but also preferential depletion leads to scale-free ne...
Scale-free networks are characterized by a degree distribution with power-law behavior and have been...
In order to explore further the underlying mechanism of scale-free networks, we study stochastic sec...
Real-world networks process structured connections since they have non-trivial vertex degree correla...
We propose a simple mechanism for generating scale-free networks with degree exponent γ = 3, where t...
Real-world networks process structured connections since they have non-trivial vertex degree correla...
An important problem in modeling networks is how to generate a randomly sampled graph with given deg...
In this paper we introduce a model of spatial network growth in which nodes are placed at randomly s...
We show how scale-free degree distributions can emerge naturally from growing networks by using rand...
We propose a novel mechanism to generate a family of deterministic small-world and scale-free networ...