Recently, the assortative mixing of complex networks has received much attention partly because of its significance in various social networks. In this paper, a new scheme to generate an assortative growth network with given degree distribution is presented using a Monte Carlo sampling method. Since the degrees of a great number of real-life networks obey either power-law or Poisson distribution, we employ these two distributions to grow our models. The models generated by this method exhibit interesting characteristics such as high average path length, high clustering coefficient and strong rich-club effects.Department of Electronic and Information Engineerin
We propose a model of an underlying mechanism responsible for the formation of assortative mixing in...
In this work we analyze the implications of using a power law distribution of vertice's quality in t...
Social networking has been a feature of human society. From the early hunter-gatherer tribes, mediev...
Abstract One of the most influential recent results in network analysis is that many natural network...
Abstract. We present a stochastic model for networks with arbitrary degree distributions and average...
Due to the recent availability of large complex networks, considerable analysis has focused on under...
In this Brief Report we present a version of a network growth model, generalized in order to describ...
International audienceThe degree distributions of complex networks are usually considered to follow ...
Social networks generally display a positively skewed degree distribution and higher values for clus...
Calculation of assortative mixing by degree in networks indicates whether nodes with similar degree ...
Networks representing social systems display specific features that put them apart from biological a...
Networks representing social systems display specific features that put them apart from biological a...
Networks with bimodal degree distribution are most robust to targeted and random attacks. We present...
AbstractMany social, biological or technological systems are recognized as complex networks with ass...
We present a simple model of network growth and solve it by writing the dynamic equations for its ma...
We propose a model of an underlying mechanism responsible for the formation of assortative mixing in...
In this work we analyze the implications of using a power law distribution of vertice's quality in t...
Social networking has been a feature of human society. From the early hunter-gatherer tribes, mediev...
Abstract One of the most influential recent results in network analysis is that many natural network...
Abstract. We present a stochastic model for networks with arbitrary degree distributions and average...
Due to the recent availability of large complex networks, considerable analysis has focused on under...
In this Brief Report we present a version of a network growth model, generalized in order to describ...
International audienceThe degree distributions of complex networks are usually considered to follow ...
Social networks generally display a positively skewed degree distribution and higher values for clus...
Calculation of assortative mixing by degree in networks indicates whether nodes with similar degree ...
Networks representing social systems display specific features that put them apart from biological a...
Networks representing social systems display specific features that put them apart from biological a...
Networks with bimodal degree distribution are most robust to targeted and random attacks. We present...
AbstractMany social, biological or technological systems are recognized as complex networks with ass...
We present a simple model of network growth and solve it by writing the dynamic equations for its ma...
We propose a model of an underlying mechanism responsible for the formation of assortative mixing in...
In this work we analyze the implications of using a power law distribution of vertice's quality in t...
Social networking has been a feature of human society. From the early hunter-gatherer tribes, mediev...