论文标题
具有任何实际或理论分布的随机增长模型
A Random Growth Model with any Real or Theoretical Degree Distribution
论文作者
论文摘要
复杂网络的程度分布通常被认为是权力法。但是，其中许多人并非如此。因此，我们提出了一个新模型，能够建立（几乎）任何想要的学位分布的随机增长网络。该度分布可以是理论上的，也可以从实际网络中提取。主要思想是将通常用于计算学位分布的复发方程式倒入，以便找到节点连接的方便附件函数 - 通常选择为线性。我们为某些经典分布计算此附件函数，例如幂律，破碎的幂律，几何和泊松分布。我们还在无向版本的Twitter网络上使用该模型，该学位分布的形状异常。我们最终表明，所选的附件函数的差异是与获得的度分布的重尾特性的密切联系。
The degree distributions of complex networks are usually considered to be power law. However, it is not the case for a large number of them. We thus propose a new model able to build random growing networks with (almost) any wanted degree distribution. The degree distribution can either be theoretical or extracted from a real-world network. The main idea is to invert the recurrence equation commonly used to compute the degree distribution in order to find a convenient attachment function for node connections - commonly chosen as linear. We compute this attachment function for some classical distributions, as the power-law, broken power-law, geometric and Poisson distributions. We also use the model on an undirected version of the Twitter network, for which the degree distribution has an unusual shape. We finally show that the divergence of chosen attachment functions is heavily links to the heavy-tailed property of the obtained degree distributions.
