We present a family of scale-free network model consisting of cliques, which is established by a simple recursive algorithm. We investigate the networks both analytically and numerically. The obtained analytical solutions show that the networks follow a power-law degree distribution, with degree exponent continuously tuned between 2 and 3. The exact expression of clustering coefficient is also provided for the networks. Furthermore, the investigation of the average path length reveals that the networks possess small-world feature. Interestingly, we find that a special case of our model can be mapped into the Yule process
We make a mapping from Sierpinski fractals to a new class of networks, the incompatibility networks,...
Generating random graphs to model networks has a rich history. In this paper, we analyze and improve...
In this paper, we propose a simple rule that generates scale-free small-world networks with tunable ...
We propose a general model of unweighted and undirected networks having the scale-free property and ...
Real networks can be classified into two categories: fractal networks and non-fractal networks. Here...
Abstract. Motivated by the hierarchial network model of E. Rav-asz, A.-L. Barabási, and T. Vicsek [...
Motivated by the hierarchial network model of E. Ravasz, A.-L. Barabási, and T. Vicsek, we introduce...
We introduce a new family of models for growing networks. In these networks new edges are preferenti...
In this paper, we present a simple model of scale-free networks that incorporates both preferential ...
We propose a simple mechanism for generating scale-free networks with degree exponent γ = 3, where t...
Scale-free networks are copious in nature and are present in diverse systems such as the nervous sys...
Fractal and multifractal properties characterize many realworld scale-free networks. Here we present...
doi:10.1088/1367-2630/9/6/177 Abstract. Fractal scaling and self-similar connectivity behaviour of s...
<p>According to the values of the scaling exponents, the seven species listed are grouped into two c...
Hierarchical networks actually have many applications in the real world. Firstly, we propose a new c...
We make a mapping from Sierpinski fractals to a new class of networks, the incompatibility networks,...
Generating random graphs to model networks has a rich history. In this paper, we analyze and improve...
In this paper, we propose a simple rule that generates scale-free small-world networks with tunable ...
We propose a general model of unweighted and undirected networks having the scale-free property and ...
Real networks can be classified into two categories: fractal networks and non-fractal networks. Here...
Abstract. Motivated by the hierarchial network model of E. Rav-asz, A.-L. Barabási, and T. Vicsek [...
Motivated by the hierarchial network model of E. Ravasz, A.-L. Barabási, and T. Vicsek, we introduce...
We introduce a new family of models for growing networks. In these networks new edges are preferenti...
In this paper, we present a simple model of scale-free networks that incorporates both preferential ...
We propose a simple mechanism for generating scale-free networks with degree exponent γ = 3, where t...
Scale-free networks are copious in nature and are present in diverse systems such as the nervous sys...
Fractal and multifractal properties characterize many realworld scale-free networks. Here we present...
doi:10.1088/1367-2630/9/6/177 Abstract. Fractal scaling and self-similar connectivity behaviour of s...
<p>According to the values of the scaling exponents, the seven species listed are grouped into two c...
Hierarchical networks actually have many applications in the real world. Firstly, we propose a new c...
We make a mapping from Sierpinski fractals to a new class of networks, the incompatibility networks,...
Generating random graphs to model networks has a rich history. In this paper, we analyze and improve...
In this paper, we propose a simple rule that generates scale-free small-world networks with tunable ...