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Can I read The Gromov–Wasserstein distance between networks and stable network invariants on EtoBox?
The Gromov–Wasserstein distance between networks and stable network invariants by Samir Chowdhury; Facundo Mémoli is a Computer Science article available to read on EtoBox.
What is The Gromov–Wasserstein distance between networks and stable network invariants about?
Abstract 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.
Who reads The Gromov–Wasserstein distance between networks and stable network invariants?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Samir Chowdhury; Facundo Mémoli
- Publisher
- Oxford University Press; Oxford University Press (OUP) (ISSN 2049-8764)
- Published
- 2019
- Language
- EN
- Field
- Computer Science (Physical Sciences)