The availability of large datasets composed of graphs creates an unprecedented need to invent novel tools in statistical learning for graph-valued random variables. To characterize the average of a sample of graphs, one can compute the sample Frechet mean and median graphs. In this paper, we address the following foundational question: does a mean or median graph inherit the structural properties of the graphs in the sample? An important graph property is the edge density; we establish that edge density is an hereditary property, which can be transmitted from a graph sample to its sample Frechet mean or median graphs, irrespective of the method used to estimate the mean or the median. Because of the prominence of the Frechet mean in graph-valued machine learning, this novel theoretical result has some significant practical consequences.
翻译:由图表组成的大型数据集的可用性使得在图表估价随机变量的统计学学习中发明新工具成为前所未有的需要。 为了描述图表样本的平均值,人们可以计算Frechet样本平均值和中位图。 在本文中,我们讨论了以下基本问题:一个中位图是否继承了样本中图表的结构属性?一个重要的图表属性是边缘密度;我们确定边缘密度是一种遗传属性,可以从图表样本中将它从Frechet平均值或中位图传递到其样本Frechet平均值或中位图中位图,而不论使用何种方法来估计平均值或中位图。由于Frechet平均值在图表估价机器学习中的突出地位,这一新理论结果产生了一些重大的实际后果。