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Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints

SIAM Journal on Mathematics of Data Science(2025)10.1137/24m1630499Source: DataRank Database

Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints is a research paper published in SIAM Journal on Mathematics of Data Science (2025). On theSindex it has a DataRank of 0. It has been cited 1 time.

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Open Access1 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 2.19)OA: goldTopics (17)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Yaqi Wu ORCID,Yanxiang Zhao ORCID,Zixuan Cang ORCID

Live enrichment

PubMed:40893388 PMC:PMC12395365 OpenAlex:https://openalex.org/W4407793173

National Science Foundation

Grant: DMS-2151934

National Science Foundation

Grant: DMS-2142500

NIH

Grant: R01GM152494

FWCI

2.19

Citation Percentile

0.8%

Citation Trend

2025

gold

journalhttps://doi.org/10.1137/24m1630499

publisherhttps://doi.org/10.1137/24m1630499

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40893388

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/12395365

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12395365/pdf/nihms-2075546.pdf

Fields of Study

Geometric Analysis and Curvature Flows

Keywords

Metric (unit)Wasserstein metricMathematical optimizationMathematicsComputer scienceApplied mathematicsEngineeringOperations managementNonconvex Optimization65K10Minimal Vertex CoverMirror-C DescentSupervised Gromov–WassersteinSupervised Optimal Transport Solver49q2228a33