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Betweenness Centrality of Fractal and Non-Fractal Scale-Free Model Networks and Tests on Real Networks

机译:分形中心性与非分形无标度模型网络及实际网络测试

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摘要

We study the betweenness centrality of fractal and non-fractal scale-free network models as well as real networks. We show that the correlation between degree and betweenness centrality C of nodes is much weaker in fractal network models compared to non-fractal models. We also show that nodes of both fractal and non-fractal scale-free networks have power law betweenness centrality distribution P(C) ~ C^δ. We find that for non-fractal scale-free networks δ = -2, and for fractal scale-free networks δ = -2 + 1/dB, where dB is the dimension of the fractal network. We supportthese results by explicit calculations on four real networks: pharmaceutical firms (N = 6776), yeast(N = 1458), WWW (N = 2526), and a sample of Internet network at AS level (N = 20566), where N is the number of nodes in the largest connected component of a network. We also study the crossover phenomenon from fractal to non-fractal networks upon adding random edges to a fractal network. We show that the crossover length ℓ*, separating fractal and non-fractal regimes, scales with dimension dB of the network as p−1/dB, where p is the density of random edges added to the network. We find that the correlation between degree and betweenness centrality increases with p.
机译:我们研究了分形和非分形无标度网络模型以及实际网络的中间性。我们表明,与非分形模型相比,分形网络模型中节点的度和中间中心度C之间的相关性要弱得多。我们还表明,分形和非分形无标度网络的节点都具有幂律中间性分布P(C)〜C ^δ。我们发现,对于非分形无标度网络,δ= -2,对于分形无标度网络,δ= -2 + 1 / dB,其中dB是分形网络的维数。我们通过在四个实际网络上进行显式计算来支持这些结果:制药公司(N = 6776),酵母菌(N = 1458),WWW(N = 2526)和AS级别的Internet网络样本(N = 20566),其中N是网络中最大连接组件中的节点数。我们还研究了向分形网络添加随机边缘后从分形网络到非分形网络的交叉现象。我们表明,分形和非分形机制的交叉长度ℓ*随网络尺寸dB的变化为p-1 / dB,其中p是添加到网络中的随机边缘的密度。我们发现,度与中间性之间的相关性随p的增加而增加。

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