论文标题
具有较高差异的随机过程产生无标度的网络
Random Processes with High Variance Produce Scale Free Networks
论文作者
论文摘要
现实世界中的网络往往是不含规模的，具有重型度分布，其集线器比经典的随机图生成方法更大。优先依恋和增长是导致这些网络的最常见的机制，并纳入了Barabási-Albert（BA）模型中。我们使用受广义中心极限定理（CLT）启发的随机停止链接过程提供替代模型，用于具有变化的参数的几何分布。 BA模型和我们随机停止的链接模型的共同特征是几何分布的混合物，这表明规模不自由网络的关键特征是高方差，而不是增长或优先附件。经典随机图模型的局限性是参数的差异较低，而无标度网络是现实世界差异的自然，预期的结果。
Real-world networks tend to be scale free, having heavy-tailed degree distributions with more hubs than predicted by classical random graph generation methods. Preferential attachment and growth are the most commonly accepted mechanisms leading to these networks and are incorporated in the Barabási-Albert (BA) model. We provide an alternative model using a randomly stopped linking process inspired by a generalized Central Limit Theorem (CLT) for geometric distributions with widely varying parameters. The common characteristic of both the BA model and our randomly stopped linking model is the mixture of widely varying geometric distributions, suggesting the critical characteristic of scale free networks is high variance, not growth or preferential attachment. The limitation of classical random graph models is low variance in parameters, while scale free networks are the natural, expected result of real-world variance.
