The availability of large scale streaming network data has reinforced the ubiquity of power-law distributions in observations and enabled precision measurements of the distribution parameters. The increased accuracy of these measurements allows new underlying generative network models to be explored. The preferential attachment model is a natural starting point for these models. This work adds additional model components to account for observed phenomena in the distributions. In this model, preferential attachment is supplemented to provide a more accurate theoretical model of network traffic. Specifically, a probabilistic complex network model is proposed using preferential attachment as well as additional parameters to describe the newly observed prevalence of leaves and unattached nodes. Example distributions from this model are generated by considering random sampling of the networks created by the model in such a way that replicates the current data collection methods.

中文翻译：

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网络流量的混合幂律模型

大规模流网络数据的可用性增强了观测中幂律分布的普遍性，并实现了分布参数的精确测量。这些测量精度的提高允许探索新的潜在生成网络模型。优先依恋模型是这些模型的自然起点。这项工作增加了其他模型组件，以解决分布中观察到的现象。在此模型中，对优先连接进行了补充，以提供更准确的网络流量理论模型。具体而言，提出了一种概率复杂网络模型，该模型使用优先附着以及附加参数来描述新观察到的叶子和未附着节点的普遍性。