Multi-Marginal Gromov-Wasserstein Transport and Barycenters
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Abstract (arXiv)
Gromov-Wasserstein (GW) distances are combinations of Gromov-Hausdorff and Wasserstein distances that allow the comparison of two different metric measure spaces (mm-spaces). Due to their invariance under measure- and distance-preserving transformations, they are well suited for many applications in graph and shape analysis. In this paper, we introduce the concept of multi-marginal GW transport between a set of mm-spaces as well as its regularized and unbalanced versions. As a special case, we discuss multi-marginal fused variants, which combine the structure information of an mm-space with label information from an additional label space. To tackle the new formulations numerically, we consider the bi-convex relaxation of the multi-marginal GW problem, which is tight in the balanced case if the cost function is conditionally negative definite. The relaxed model can be solved by an alternating minimization, where each step can be performed by a multi-marginal Sinkhorn scheme. We show relations of our multi-marginal GW problem to (unbalanced, fused) GW barycenters and present various numerical results, which indicate the potential of the concept.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2022.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 4.
9 papers.
- 2023Hierarchical Multi-Marginal Optimal Transport for Network Alignmentcited by 24
- 2024Contrasting Multiple Representations with the Multi-Marginal Matching Gapcited by 8
- 2025Joint Metric Space Embedding by Unbalanced OT with Gromov-Wasserstein Marginal Penalizationcited by 2
- 2025Neural Estimation for Scaling Entropic Multimarginal Optimal Transportcited by 2
- 2023Optimal transport for automatic alignment of untargeted metabolomic datacited by 1
- 2026Binding Multiple Modalities via Multimodal Wasserstein Barycentercited by 1
- 2026On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport planscited by 0
- 2026Gromov-Wasserstein Quantization and Clustering: Structure, Rates, and Algorithmscited by 0
- 2026A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Datacited by 0
- 2026Binding Multiple Modalities via Multimodal Wasserstein Barycenter27 Sep 2026
- 2026A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data15 Aug 2026
- 2026Gromov-Wasserstein Quantization and Clustering: Structure, Rates, and Algorithms11 Aug 2026
- 2026On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport plans18 Feb 2026
- 2025Neural Estimation for Scaling Entropic Multimarginal Optimal Transport31 May 2025
- 2025Joint Metric Space Embedding by Unbalanced OT with Gromov-Wasserstein Marginal Penalization11 Feb 2025
- 2024Contrasting Multiple Representations with the Multi-Marginal Matching Gap29 May 2024
- 2023Hierarchical Multi-Marginal Optimal Transport for Network Alignment6 Oct 2023
- 2023Optimal transport for automatic alignment of untargeted metabolomic data5 Jun 2023