Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in recent literature. Most of these algorithms are based on simple intuitions that associate the full features of connectivity patterns with specific values of only one or two network metrics. Substantive conclusions are crucially dependent on this association holding true. However, the extent to which this simple intuition holds true is not yet known. In this paper, we examine the association between the connectivity patterns that a network sampling algorithm aims to generate and the connectivity patterns of the generated networks, measured by an existing set of popular network metrics. We find that different network sampling algorithms can yield networks with similar connectivity patterns. We also find that the alternative algorithms for the same connectivity pattern can yield networks with different connectivity patterns. We argue that conclusions based on simulated network studies must focus on the full features of the connectivity patterns of a network instead of on the limited set of networkmetrics for a specific network type. This fact has important implications for network data analysis: for instance, implications related to the way significance is currently assessed.

生成具有所需属性的随机网络样本的方法是分析社会、生物和信息网络的重要工具。在最近的文献中，基于算法的采样网络方法受到了广泛关注。这些算法中的大多数都基于简单的直觉，这些直觉将连接模式的全部特征与仅一两个网络指标的特定值相关联。实质性结论关键取决于这种关联是否成立。然而，这种简单的直觉在多大程度上成立尚不得而知。在本文中，我们检查了网络采样算法旨在生成的连接模式与生成的网络的连接模式之间的关联，通过一组现有的流行网络指标来衡量。我们发现不同的网络采样算法可以产生具有相似连接模式的网络。我们还发现，相同连接模式的替代算法可以产生具有不同连接模式的网络。我们认为，基于模拟网络研究的结论必须侧重于网络连接模式的全部特征，而不是针对特定网络类型的有限网络度量集。这一事实对网络数据分析具有重要意义：例如，与当前评估重要性的方式相关的意义。我们认为，基于模拟网络研究的结论必须侧重于网络连接模式的全部特征，而不是针对特定网络类型的有限网络度量集。这一事实对网络数据分析具有重要意义：例如，与当前评估重要性的方式相关的意义。我们认为，基于模拟网络研究的结论必须侧重于网络连接模式的全部特征，而不是针对特定网络类型的有限网络度量集。这一事实对网络数据分析具有重要意义：例如，与当前评估重要性的方式相关的意义。