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CHANGING CORRELATIONS IN NETWORKS: ASSORTATIVITY AND DISSORTATIVITY

机译:网络中不断变化的相关性:同化性和非同化性

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摘要

To analyze the role of correlations in networks, in particular, assorta-tivity and dissortativity, we introduce two algorithms which respectively produce assortative and dissortative mixing to a desired degree. In both procedures this degree is governed by a single parameter p. Varying this parameter, one can change correlations in networks without modifying their degree distribution to produce new versions ranging from fully random (p = 0) to totally assortative or dissortative (p = 1), depending on the algorithm used. We discuss the properties of networks emerging when applying our algorithms to a Barabasi-Albert scale-free construction. In spite of having exactly the same degree distribution, different correlated networks exhibit different geometrical and transport properties. Thus, the average path length and clustering coefficient, as well as the shell structure and percolation properties change significantly when modifying correlations.
机译:为了分析相关性在网络中的作用,特别是分类性和可分类性,我们介绍了两种算法,它们分别产生所需程度的分类和分类混合。在两个过程中,此程度均由单个参数p控制。改变此参数,就可以更改网络中的相关性,而无需修改其度数分布,以产生从完全随机(p = 0)到完全分类或非分类(p = 1)的新版本,具体取决于所使用的算法。当我们将算法应用于Barabasi-Albert无标度构造时,我们讨论了新兴网络的特性。尽管具有完全相同的度数分布,但不同的相关网络仍显示出不同的几何和传输特性。因此,当修改相关性时,平均路径长度和聚类系数以及壳结构和渗滤属性会发生显着变化。

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