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The Gromov-Wasserstein (GW) framework adapts ideas from optimal transport to allow for the comparison of probability distributions defined on different metric spaces.
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Parés, F., Gasulla, D.G., Vilalta, A., Moreno, J., Ayguadé, E., Labarta, J., Cortés, U., Suzumura, T.: Fluid communities: a competitive, scalable and diverse community detection algorithm. In: International Conference on Complex Networks and their Applications. pp. 229–240. Springer (2017)
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Alvarez-Melis, D., Jaakkola, T.: Gromov-Wasserstein alignment of word embedding spaces. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 1881–1890 (2018)
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Garg, V., Jaakkola, T.: Solving graph compression via optimal transport. In: Advances in Neural Information Processing Systems. vol. 32 (2019)
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Chowdhury, S., Needham, T.: Gromov-Wasserstein averaging in a Riemannian framework. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. pp. 842–843 (2020)
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Demetci, P., Santorella, R., Sandstede, B., Noble, W.S., Singh, R.: Gromov-Wasserstein optimal transport to align single-cell multi-omics data. BioRxiv (2020)
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Chowdhury, S., Needham, T.: Generalized spectral clustering via Gromov-Wasserstein learning. In: International Conference on Artificial Intelligence and Statistics. pp. 712–720. PMLR (2021)
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