Geometry of Graph Partitions via Optimal Transport
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Abstract (arXiv)
We define a distance metric between partitions of a graph using machinery from optimal transport. Our metric is built from a linear assignment problem that matches partition components, with assignment cost proportional to transport distance over graph edges. We show that our distance can be computed using a single linear program without precomputing pairwise assignment costs and derive several theoretical properties of the metric. Finally, we provide experiments demonstrating these properties empirically, specifically focusing on its value for new problems in ensemble-based analysis of political districting plans.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2019.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2023A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Datacited by 11
- 2021Unsupervised Ground Metric Learning using Wasserstein Singular Vectorscited by 5
- 2022Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Datacited by 1
- 2024Graph Cuts with Arbitrary Size Constraints Through Optimal Transportcited by 1
- 2022Implications of Distance over Redistricting Maps: Central and Outlier Mapscited by 0
- 2026Fused Gromov-Wasserstein Distance with Feature Selectioncited by 0
- 2026Fused Gromov-Wasserstein Distance with Feature Selection12 May 2026
- 2024Graph Cuts with Arbitrary Size Constraints Through Optimal Transport7 Feb 2024
- 2023A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data12 Mar 2023
- 2022Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data17 May 2022
- 2022Implications of Distance over Redistricting Maps: Central and Outlier Maps2 Mar 2022
- 2021Unsupervised Ground Metric Learning using Wasserstein Singular Vectors11 Feb 2021