标题： 图 GOSPA 度量族，用于不同大小的图 显示英文标题

标题： A family of graph GOSPA metrics for graphs with different sizes

摘要： 本文提出了一种图度量家族，用于测量不同大小的图之间的距离。 所提出的度量家族定义了图广义最优子模式分配（GOSPA）度量的一般形式，并且也被证明满足度量属性。 类似于图GOSPA度量，所提出的图GOSPA度量家族也对两个图之间已分配节点的节点属性成本以及未分配节点的数量进行惩罚。 然而，所提出的度量家族比图GOSPA度量提供了更一般的边不匹配惩罚。 本文还表明，可以使用线性规划近似计算图GOSPA度量家族。 进行了仿真实验，以说明在不同超参数选择下所提出的图GOSPA度量家族的特性。 所提出的图GOSPA度量家族在分类任务中的优势也在真实世界数据集上得到了展示。 显示英文摘要

摘要： This paper proposes a family of graph metrics for measuring distances between graphs of different sizes. The proposed metric family defines a general form of the graph generalised optimal sub-pattern assignment (GOSPA) metric and is also proved to satisfy the metric properties. Similarly to the graph GOSPA metric, the proposed graph GOSPA metric family also penalises the node attribute costs for assigned nodes between the two graphs, and the number of unassigned nodes. However, the proposed family of metrics provides more general penalties for edge mismatches than the graph GOSPA metric. This paper also shows that the graph GOSPA metric family can be approximately computed using linear programming. Simulation experiments are performed to illustrate the characteristics of the proposed graph GOSPA metric family with different choices of hyperparameters. The benefits of the proposed graph GOSPA metric family for classification tasks are also shown on real-world datasets.