Archaeologists are increasingly interested in networks constructed from site assemblage data, in which weighted network ties reflect sites’ assemblage similarity. Equivalent networks would arise in other scientific fields where actors’ similarity is assessed by comparing distributions of observed counts, so the assemblages studied here can represent other kinds of distributions in other domains. One concern with such work is that sampling variability in the assemblage network and, in turn, sampling variability in measures calculated from the network must be recognized in any comprehensive analysis. In this study, we investigated the use of the bootstrap as a means of estimating sampling variability in measures of assemblage networks. We evaluated the performance of the bootstrap in simulated assemblage networks, using a probability structure based on the actual distribution of sherds of ceramic wares in a region with 25 archaeological sites. Results indicated that the bootstrap was successful in estimating the true sampling variability of eigenvector centrality for the 25 sites. This held both for centrality scores and for centrality ranks, as well as the ratio of first to second eigenvalues of the network (similarity) matrix. Findings encourage the use of the bootstrap as a tool in analyses of network data derived from counts.

考古学家对根据站点组合数据构建的网络越来越感兴趣，其中加权的网络联系反映了站点的组合相似性。等效网络将出现在其他科学领域中，其中通过比较观察到的计数的分布来评估参与者的相似性，因此此处研究的组合可以表示其他领域的其他类型的分布。此类工作的一个关注点是，在任何全面分析中，都必须认识到组合网络中的样本可变性，进而从网络中计算出的度量中的样本可变性。在这项研究中，我们调查了使用引导程序作为估计组合网络度量中的抽样变异性的方法。我们评估了引导程序在模拟组合网络中的性能，使用基于在25个考古遗址的地区陶瓷器皿实际分布的概率结构。结果表明，引导程序成功地估计了25个位点的特征向量中心性的真实采样变异性。这对于中心性得分和中心性排名，以及网络（相似性）矩阵的第一特征值与第二特征值之比均成立。发现鼓励使用引导程序作为对从计数得出的网络数据进行分析的工具。以及网络（相似度）矩阵的第一特征值与第二特征值之比。发现鼓励使用引导程序作为对从计数得出的网络数据进行分析的工具。以及网络（相似度）矩阵的第一特征值与第二特征值之比。发现鼓励使用引导程序作为对从计数得出的网络数据进行分析的工具。