网络间的Gromov-Wasserstein距离与稳定网络不变量
The Gromov-Wasserstein distance between networks and stable network invariants
摘要：
我们定义了一个度量-网络Gromov-Wasserstein距离-加权，有向网络，是敏感的离群值的存在。除了证明其理论性质外，我们还提供了基于最佳传输的网络不变量，这些网络不变量通过下界近似这个距离。我们在一系列模拟网络数据集和真实世界的全球双边移民数据集上测试这些方法。对于我们的模拟，我们定义了一个基于随机块模型的网络生成模型。这可能是独立的利益为基准的目的。
英文摘要：
We define a metric---the network Gromov-Wasserstein distance---on weighted, directed networks that is sensitive to the presence of outliers. In addition to proving its theoretical properties, we supply network invariants based on optimal transport that approximate this distance by means of lower bounds. We test these methods on a range of simulated network datasets and on a dataset of real-world global bilateral migration. For our simulations, we define a network generative model based on the stochastic block model. This may be of independent interest for benchmarking purposes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
