During the past decades, power-law distributions have played a significant role in analyzing the topology of scale-free networks. However, in the observation of degree distributions in practical networks and other nonuniform distributions such as the wealth distribution, we discover that, there exists a peak at the beginning of most real distributions, which cannot be accurately described by a monotonic decreasing power-law distribution. To better describe the real distributions, in this paper, we propose a subnormal distribution derived from evolving networks with variable elements and study its statistical properties for the first time. By utilizing this distribution, we can precisely describe those distributions commonly existing in the real world, e.g., distributions of degree in social networks and personal wealth. Additionally, we fit connectivity in evolving networks and the data observed in the real world by the proposed subnormal distribution, resulting in a better performance of fitness.

中文翻译：

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来自具有可变元素的演化网络的次正态分布

在过去的几十年中，幂律分布在分析无标度网络的拓扑结构方面发挥了重要作用。然而，在观察实际网络中的度数分布和其他非均匀分布（例如财富分布）时，我们发现，在大多数实际分布的开始处都存在一个峰值，无法用单调递减的幂律分布准确地描述该峰值。 。为了更好地描述真实分布，在本文中，我们提出了从具有可变元素的演化网络中导出的次正态分布，并首次研究了其统计特性。通过利用这种分布，我们可以精确地描述现实世界中通常存在的那些分布，例如，社交网络和个人财富的度数分布。此外，