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Edge Balance Ratio: Power Law From Vertices to Edges in Directed Complex Network

机译:边缘平衡比:定向复杂网络中从顶点到边缘的幂律

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摘要

Power law distribution is common in real-world networks including online social networks. Many studies on complex networks focus on the characteristics of vertices, which are always proved to follow the power law. However, few researches have been done on edges in directed networks. In this paper, edge balance ratio is firstly proposed to measure the balance property of edges in directed networks. Based on edge balance ratio, balance profile and positivity are put forward to describe the balance level of the whole network. Then the distribution of edge balance ratio is theoretically analyzed. In a directed network whose vertex in-degree follows the power law with scaling exponent $gamma$, it is proved that the edge balance ratio follows a piecewise power law, with the scaling exponent of each section linearly dependent on $gamma$. The theoretical analysis is verified by numerical simulations. Moreover, the theoretical analysis is confirmed by statistics of real-world online social networks, including Twitter network with 35 million users and Sina Weibo network with 110 million users.
机译:幂律分布在包括在线社交网络在内的现实世界中很常见。关于复杂网络的许多研究都集中在顶点的特征上,顶点的特征总是被证明遵循幂定律。但是,针对定向网络边缘的研究很少。本文首先提出边缘平衡比来衡量有向网络中边缘的平衡特性。基于边缘平衡比,提出了平衡轮廓和正性来描述整个网络的平衡水平。然后从理论上分析了边缘平衡比的分布。在有向网络中,其顶点度服从幂定律,并且缩放比例为$ gamma $,证明了边缘平衡比遵循分段幂律,每个部分的缩放比例指数线性地取决于$ gamma $。理论分析得到了数值模拟的验证。此外,理论分析得到了现实世界在线社交网络的统计证实,包括拥有3500万用户的Twitter网络和拥有1.1亿用户的新浪微博网络。

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