Paper Citation Record · LEDGER

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs

As of 9 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2506.12025.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.12025 v3

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:24:53.081551Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:38:24.506461Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T06:39:43.775445Z

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation da648c3c-7b8b-4626-9f7d-86ff0803b8eb · outbound

This paper cites Op- tuna: A next-generation hyperparameter optimization framework.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Op- tuna: A next-generation hyperparameter optimization framework

Reference 1

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Observation 36b55d71-0c53-437f-985f-9077cdee1c5d · outbound

This paper cites Meta Optimal Transport.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Meta Optimal Transport

Reference 2

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Observation 0b0b085b-bf7a-4a77-b1ea-e6d0f0eb4bea · outbound

This paper cites Tutorial on amortized optimization.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Tutorial on amortized optimization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:58.469274Z

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Observation 8476041a-ad51-4d36-aec2-aea8fa13b103 · outbound

This paper cites A limited memory algorithm for bound constrained optimization.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs A limited memory algorithm for bound constrained optimization

Reference 4

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This paper cites Partial gromov-wasserstein with appli- cations on positive-unlabeled learning.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Partial gromov-wasserstein with appli- cations on positive-unlabeled learning

Reference 5

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Observation 94eaa7e8-29b6-41d9-8a7d-619b4d4b44ef · outbound

This paper cites Unbalanced opti- mal transport through non-negative penalized linear regression.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Unbalanced opti- mal transport through non-negative penalized linear regression

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:58.118865Z

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Observation 603abba3-2210-45e6-90ed-3c70aac7ecb3 · outbound

This paper cites Kernel operations on the gpu, with autodiff, without memory overflows.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Kernel operations on the gpu, with autodiff, without memory overflows

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:57.893741Z

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Observation 01dfc877-d863-4cea-bb5a-428bde753ef6 · outbound

This paper cites Unbalanced optimal transport: Dynamic and kantorovich formulations.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Unbalanced optimal transport: Dynamic and kantorovich formulations

Reference 8

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Observation 5c05f0d5-a491-4c4e-88b0-04d011fddbb6 · outbound

This paper cites Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H

Reference 9

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Observation 4c4f42db-2c5a-41e9-92ac-62b30cf35ca5 · outbound

This paper cites Graph- context attention networks for size-varied deep graph matching.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Graph- context attention networks for size-varied deep graph matching

Reference 10

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Observation 87db3f91-d9cc-4cc7-a5f6-a9eae1f60131 · outbound

This paper cites Neural Optimal Transport.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Neural Optimal Transport

Reference 11

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Observation dc23062f-e4eb-4b3d-98b4-c6d3750ce7d3 · outbound

This paper cites Sigma: Semantic-complete graph matching for domain adaptive object detection.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Sigma: Semantic-complete graph matching for domain adaptive object detection

Reference 12

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Observation 53ed3991-a0ef-4e78-baae-e01aff02e114 · outbound

This paper cites Graph matching networks for learning the similarity of graph structured objects.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Graph matching networks for learning the similarity of graph structured objects

Reference 13

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Observation 3c8c676a-721e-4375-b56b-672e726b885c · outbound

This paper cites Multilevel graph matching networks for deep graph similarity learning.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Multilevel graph matching networks for deep graph similarity learning

Reference 14

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Observation 532160fc-f92a-4106-b501-3c895400a160 · outbound

This paper cites Self-supervised learning of visual graph matching.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Self-supervised learning of visual graph matching

Reference 15

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Observation f1d1022f-7211-4a66-bfbb-4c007133bfd0 · outbound

This paper cites A survey for the quadratic assignment problem.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs A survey for the quadratic assignment problem

Reference 16

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Observation 48a030ec-c640-4205-9b55-5f95e4fed7bb · outbound

This paper cites Gromov–wasserstein distances and the metric approach to object matching.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Gromov–wasserstein distances and the metric approach to object matching

Reference 17

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Observation b74a57d2-5551-4dda-8c4a-ed9416ece763 · outbound

This paper cites Graph node matching for edit distance.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Graph node matching for edit distance

Reference 18

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Observation 60c2f57f-2182-43c4-94cc-302d98f79f56 · outbound

This paper cites Neural gromov-wasserstein optimal transport.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Neural gromov-wasserstein optimal transport

Reference 19

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Observation 73889b24-ff48-4c23-95f7-8bc5963bad3d · outbound

This paper cites Functional maps: a flexible representation of maps between shapes.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Functional maps: a flexible representation of maps between shapes

Reference 20

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Observation 08f2e4f2-8f3c-4ee5-8db0-1006a16755b3 · outbound

This paper cites Gromov-wasserstein averaging of kernel and distance matrices.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Gromov-wasserstein averaging of kernel and distance matrices

Reference 21

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Observation 9f5d8ff9-6249-473b-8f0b-90bf1da4dacf · outbound

This paper cites Computing graph edit distance via neural graph matching.Proceedings of the VLDB Endowment, 16(8):1817–1829, 2023.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Computing graph edit distance via neural graph matching.Proceedings of the VLDB Endowment, 16(8):1817–1829, 2023

Reference 22

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Observation 109782ef-cfc5-4902-977d-14dde9ca0528 · outbound

This paper cites Individual brain charting, a high-resolution fmri dataset for cognitive mapping.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Individual brain charting, a high-resolution fmri dataset for cognitive mapping

Reference 23

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Observation 8a5d70be-c7d1-482b-a9ad-b3068bf59a19 · outbound

This paper cites Optimal transport for multi- source domain adaptation under target shift.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Optimal transport for multi- source domain adaptation under target shift

Reference 24

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Observation 04a33c9f-3553-4d30-a7fe-2dc5daaa4cb7 · outbound

This paper cites Superglue: Learning feature matching with graph neural networks.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Superglue: Learning feature matching with graph neural networks

Reference 25

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Observation d4d67a8c-48f9-4b31-8cbc-59ec3c455756 · outbound

This paper cites Large-Scale Optimal Transport and Mapping Estimation.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Large-Scale Optimal Transport and Mapping Estimation

Reference 26

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This paper cites The unbalanced gromov wasserstein distance: Conic formulation and relaxation.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs The unbalanced gromov wasserstein distance: Conic formulation and relaxation

Reference 27

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Observation 72c83e5c-7106-4053-9983-be443bbfb943 · outbound

This paper cites Wasserstein propa- gation for semi-supervised learning.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Wasserstein propa- gation for semi-supervised learning

Reference 28

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Observation f6c07377-dd57-415a-b30d-928a218c3fd2 · outbound

This paper cites Which fmri clustering gives good brain parcellations? Frontiers in neuroscience, 8:167, 2014.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Which fmri clustering gives good brain parcellations? Frontiers in neuroscience, 8:167, 2014

Reference 29

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This paper cites Aligning individual brains with fused unbalanced gromov wasserstein.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Aligning individual brains with fused unbalanced gromov wasserstein

Reference 30

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Observation 9139461f-2e4d-4cf4-a282-7e4d97fdb892 · outbound

This paper cites Optimal transport for structured data with application on graphs.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Optimal transport for structured data with application on graphs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:55.051719Z

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Observation 3680a61c-7c76-410d-ac65-43e188369151 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 32

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This paper cites Discrete cycle-consistency based unsupervised deep graph matching.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Discrete cycle-consistency based unsupervised deep graph matching

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:54.892542Z

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Observation e1d1938b-1df9-45fd-9359-ad04f618277e · outbound

This paper cites Fused gromov-wasserstein distance for structured objects.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Fused gromov-wasserstein distance for structured objects

Reference 34

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Observation 7a98b4c9-7f54-45b9-8304-6f964fbb7055 · outbound

This paper cites Deep learning of partial graph matching via differentiable top-k.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Deep learning of partial graph matching via differentiable top-k

Reference 35

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This paper cites Learning combinatorial embedding networks for deep graph matching.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Learning combinatorial embedding networks for deep graph matching

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:54.429986Z

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Observation 2ef6f582-a8ed-4ffc-9d26-8ecfd47c24a4 · outbound

This paper cites Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:54.293481Z

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Observation df7511df-f430-43c9-bb0f-d425bbb55a7b · outbound

This paper cites Neural entropic gromov-wasserstein alignment.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Neural entropic gromov-wasserstein alignment

Reference 38

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Observation 98d0be43-04a9-4a31-8836-dcef4713fb56 · outbound

This paper cites A fast proximal point method for computing exact wasserstein distance.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs A fast proximal point method for computing exact wasserstein distance

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:54.133623Z

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Observation be0d4955-2619-4569-a599-2b5076a9b983 · outbound

This paper cites Gromov-wasserstein learning for graph matching and node embedding.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Gromov-wasserstein learning for graph matching and node embedding

Reference 40

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This paper cites Deep learning of graph matching.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Deep learning of graph matching

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:53.962584Z

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Observation 55dec0cf-bcbe-4997-99ac-9cff091b4ef4 · outbound

This paper cites Gromov– wasserstein distances: Entropic regularization, duality and sample complexity.

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs Gromov– wasserstein distances: Entropic regularization, duality and sample complexity

Reference 42

Resolution: verified exact; raw_fallback, observed 2026-08-07T15:24:53.434382Z

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Pith citing papers

Observation b6b33dcd-5072-410b-b226-5400f9f59762 · inbound

Learning fMRI activations dictionaries across individual geometries via optimal transport cites this paper.

Learning fMRI activations dictionaries across individual geometries via optimal transport Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:39:43.777205Z

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