To characterize the "average" of a sample of graphs, one can compute the sample Frechet mean (or median) graph, which provides an interpretable summary of the graph sample. In this paper, we address the following foundational question: does the mean or median graph inherit the structural properties of the graphs in the sample? An important graph property is the edge density. Because sparse graphs provide prototypical models for real networks, one would like to guarantee that the edge density be preserved when computing the sample mean (or median). In this paper, we prove that the edge density is an hereditary property, which can be transmitted from a graph sample to its sample Frechet mean (or median), irrespective of the method used to estimate the mean or the median. Specifically, we prove the following result: the number of edges of the Frechet mean (or median) graph of a set of graphs is bounded by the maximal number of edges amongst all the graphs in the sample. We prove the result for the graph Hamming distance, and the spectral adjacency pseudometric, using very different arguments.
翻译:为了确定图表样本的“平均”特征,人们可以计算Frechet样本平均值(或中位)图,该样本提供了可解释的图表样本摘要。在本文中,我们处理以下基本问题:平均值或中位图是否继承了样本中图表的结构属性?重要的图表属性是边缘密度。由于稀疏图为真实网络提供了原型模型,人们希望在计算样本平均值(或中位数)时保证边缘密度得到保留。在本文中,我们证明边缘密度是一种遗传属性,可以从图表样本中传输到Frechet样本中位数(或中位数),而不论使用何种方法来估计平均值或中位数。具体地说,我们证明以下结果:一组图表的边缘数由样本中所有图表中的最大边缘数(或中位数)所捆绑定。我们用非常不同的论据证明了图表 Hamming 距离的结果,以及光谱的伪数。