Preferential attachment is an appealing edge generating mechanism for modeling social networks. It provides both an intuitive description of network growth and an explanation for the observed power laws in degree distributions. However, there are often difficulties fitting parametric network models to data due to either model error or data corruption. In this paper, we consider semi-parametric estimation based on an extreme value approach that begins by estimating tail indices of the power laws of in- and out-degree for the nodes of the network using nodes with large in- and out-degree. This method uses tail behavior of both the marginal and joint degree distributions. We compare the extreme value method with the existing parametric approaches and demonstrate how it can provide more robust estimates of parameters associated with the network when the data are corrupted or when the model is misspecified.

中文翻译：

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极值估计方法对网络数据有用吗？

优先附件是用于对社交网络进行建模的一种吸引人的边缘生成机制。它既提供了网络增长的直观描述，又提供了度分布中观察到的功率定律的解释。但是，由于模型错误或数据损坏，通常难以将参数网络模型拟合到数据。在本文中，我们考虑基于极值方法的半参数估计，该方法首先使用具有较大入度和出度的节点来估计网络节点的入度和出度的幂律的尾部索引。此方法使用边际和联合度分布的尾部行为。