To characterize the location (mean, median) of a set of graphs, one needs a notion of centrality that is adapted to metric spaces, since graph sets are not Euclidean spaces. A standard approach is to consider the Fr\'echet mean. In this work, we equip a set of graph with the pseudometric defined by the $\ell_2$ norm between the eigenvalues of their respective adjacency matrix . Unlike the edit distance, this pseudometric reveals structural changes at multiple scales, and is well adapted to studying various statistical problems on sets of graphs. We describe an algorithm to compute an approximation to the Fr\'echet mean of a set of undirected unweighted graphs with a fixed size.
翻译:为了确定一组图表的位置(平均值、中位数),人们需要一种适合度量空间的中心点概念,因为图形组不是欧几里德空间。一个标准的方法是考虑Fr\'echet平均值。在这项工作中,我们用一套图表的伪度来装备一套图表,其义性参数以$/ell_2美元的标准定义,这些图形组的相邻矩阵的均值。与编辑距离不同,这种伪度显示多个尺度的结构性变化,并且非常适合研究数组图表中的各种统计问题。我们描述一种算法,以计算一组固定大小的无方向非加权图表的近似值。