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New Deterministic Model of Evolving Trinomial Networks

机译:演化三项式网络的新确定性模型

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We provide a new model of attributed networks where label of each vertex is a partition of integer n into at most m integer parts and all labels are different. The metric in the space of (n, m)-partitions is introduced. First, we investigate special class of the trinomial (m~2, m)-partitions as a base for synthesis of networks G(m). It turns out that algorithmic complexity (the shortest computer program that produces G(m) upon halting) of these networks grows with m as log m only. Numerical simulations of simple graphs for trinomial (m~2,m)-partition families (m = 3,4,..., 9) allows to estimate topological parameters of the graphs-clustering coefficients, cliques distribution, vertex degree distribution-and to show existence of such effects as scale-free and self-similarity for evolving networks. Since the model under consideration is completely deterministic, these results put forward new mode of thought about mechanisms of similarity, preferential attachment and popularity of complex networks. In addition, we obtained some numerical results relating robust behavior of the networks to disturbances like deleting nodes or cliques.
机译:我们提供了一种新的属性网络模型,其中每个顶点的标签是将整数n划分为最多m个整数部分,并且所有标签都是不同的。介绍了(n,m)分区空间中的度量。首先,我们研究三项式(m〜2,m)分区的特殊类,作为网络G(m)综合的基础。事实证明,这些网络的算法复杂度(停止时产生G(m)的最短计算机程序)仅以m为log m增长。对三项(m〜2,m)-分区族(m = 3,4,...,9)的简单图的数值模拟可以估算图的拓扑参数-聚类系数,集团分布,顶点度分布-和以显示演化网络的无标度和自相似性等效应的存在。由于所考虑的模型是完全确定性的,因此这些结果提出了关于复杂网络的相似性,优先附着和流行机制的新思路。此外,我们获得了一些数值结果,这些结果将网络的鲁棒性行为与诸如删除节点或集团之类的干扰相关联。

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