A family of graph GOSPA metrics for graphs with different sizes
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Abstract (arXiv)
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.
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References · 7
- 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
- 2019Gromov-Wasserstein Learning for Graph Matching and Node Embeddingcited by 126
- 2019GOT: An Optimal Transport framework for Graph comparisoncited by 64
- 2016Generalized optimal sub-pattern assignment metriccited by 33
- 2016A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithmscited by 17
- 2016A metric for sets of trajectories that is practical and mathematically consistentcited by 7
- 2023Graph GOSPA metric: a metric to measure the discrepancy between graphs of different sizescited by 3
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