{"as_of":"2026-08-09T16:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f7f1309d4d9a6e47c858ef90008714d58262b5b73714896f1dee824b7d0dff58","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:24:53.081551Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T06:38:24.506461Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-21T06:39:43.775445Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"cited_work":{"arxiv_id":"2506.12025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.12025","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unsupervised learning for optimal transport plan prediction between unbalanced graphs","venue":null,"work_id":"342e9947-bc47-46a6-8138-fddde974dbd2","year":2025},"citing_paper":{"arxiv_id":"2605.20883","last_updated":"2026-05-20T08:22:38Z","snapshot_observed_at":"2026-08-03T04:01:35.950821Z","submitted_at":"2026-05-20T08:22:38Z","title":"Learning fMRI activations dictionaries across individual geometries via optimal transport","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-21T06:38:24.506461Z"},"links":{"cited_paper":"/paper/2506.12025","citing_paper":"/paper/2605.20883"},"observation_digest":"sha256:57bcf61d97d32b95cd6b508b1aa99c0d6e4da3f1512de86d6736b271a9104ea7","observation_id":"b6b33dcd-5072-410b-b226-5400f9f59762","resolution":{"observed_at":"2026-05-21T06:39:43.777205Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.12025/citation-record","integrity":"/paper/2506.12025/integrity","json":"/paper/2506.12025/citation-record.json","paper":"/paper/2506.12025"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:58.575032Z","title":"Op- tuna: A next-generation hyperparameter optimization framework","venue":null,"work_id":"619947f4-cd14-4b4a-9d06-bed8c636481b","year":2019},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:49.257647Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:73dc611bc655d1615955dbdd1adee7bb740d89dd1826c0a408c5f3b84d23dabe","observation_id":"da648c3c-7b8b-4626-9f7d-86ff0803b8eb","resolution":{"observed_at":"2026-08-07T15:24:58.663738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05262","last_updated":"2023-06-02T21:45:43Z","snapshot_observed_at":"2026-07-06T13:19:36.839011Z","submitted_at":"2022-06-10T17:59:07Z","title":"Meta Optimal Transport","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.05262","snapshot_observed_at":"2026-08-07T15:24:49.312767Z","title":"Meta optimal transport","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:49.312767Z"},"links":{"cited_paper":"/paper/2206.05262","citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:9a2c1ef35f32fb258c01a2a3decdfb8247760ca9cbee4df2dfb6936bcb53c036","observation_id":"36b55d71-0c53-437f-985f-9077cdee1c5d","resolution":{"observed_at":"2026-08-07T15:24:49.312767Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:58.405780Z","title":"Tutorial on amortized optimization","venue":null,"work_id":"eb8551e4-fc75-43e3-9463-5a49f905ce3e","year":2023},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:49.382880Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:66e20c26d77fd6ae8d1306e293bd9a12a2a5b0efab6d91b330a3cffc5c9d3b78","observation_id":"0b0b085b-bf7a-4a77-b1ea-e6d0f0eb4bea","resolution":{"observed_at":"2026-08-07T15:24:58.469274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:49.479917Z","title":"A limited memory algorithm for bound constrained optimization","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:49.479917Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:8088ca7250d82d38996db94c8ccd2c5fae8b4034398e23b8cb33155ab9d30978","observation_id":"8476041a-ad51-4d36-aec2-aea8fa13b103","resolution":{"observed_at":"2026-08-07T15:24:49.479917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:58.235610Z","title":"Partial gromov-wasserstein with appli- cations on positive-unlabeled learning","venue":null,"work_id":"b4add93f-c48f-47eb-b970-47f5409383e6","year":2020},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:49.574595Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:ad16b224f481deb96a51fa893e93c8cc1f334780786221c614bd13d866ee2229","observation_id":"db2e8069-4144-418c-8544-725d86210f14","resolution":{"observed_at":"2026-08-07T15:24:58.319841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:58.032850Z","title":"Unbalanced opti- mal transport through non-negative penalized linear regression","venue":null,"work_id":"3b014ed0-e3f2-4753-8de5-2334919fbfe1","year":2021},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:49.710564Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:b5fcc1fa8200891d5302a9ee2eb71e5853f18b702c48fc167392ab72df7c8ae0","observation_id":"94eaa7e8-29b6-41d9-8a7d-619b4d4b44ef","resolution":{"observed_at":"2026-08-07T15:24:58.118865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:57.811822Z","title":"Kernel operations on the gpu, with autodiff, without memory overflows","venue":null,"work_id":"9c105a06-cbf3-4ebf-a346-734c809fb047","year":2021},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:49.815996Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:7bd7cd2dafac83304a5710e0d9678625a6f8973121ee15afe92bd2e9be1bcd45","observation_id":"603abba3-2210-45e6-90ed-3c70aac7ecb3","resolution":{"observed_at":"2026-08-07T15:24:57.893741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:57.684063Z","title":"Unbalanced optimal transport: Dynamic and kantorovich formulations","venue":null,"work_id":"273e6494-4883-4ebd-8e09-5dbfcaa6de4f","year":2018},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:49.909019Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:c63b97fdb6210b60eef1133cfa90da21e047f2147ebd563bdf084b0fcad4dfc8","observation_id":"01dfc877-d863-4cea-bb5a-428bde753ef6","resolution":{"observed_at":"2026-08-07T15:24:57.749527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:50.006930Z","title":"Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.006930Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:bd95cd2ad9b111320ec61266ef51bf939907b789a47f2466f5b92fd92d910362","observation_id":"5c05f0d5-a491-4c4e-88b0-04d011fddbb6","resolution":{"observed_at":"2026-08-07T15:24:50.006930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:57.484374Z","title":"Graph- context attention networks for size-varied deep graph matching","venue":null,"work_id":"1da26552-4d2d-4f55-b139-286c83b3632f","year":2022},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.114595Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:8778b068e6092bea29663d77a1bf2c846deac33f1009a38d0153c276962d7a9c","observation_id":"4c4f42db-2c5a-41e9-92ac-62b30cf35ca5","resolution":{"observed_at":"2026-08-07T15:24:57.554757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12220","last_updated":"2023-03-01T13:38:35Z","snapshot_observed_at":"2026-08-05T10:35:13.239357Z","submitted_at":"2022-01-28T16:24:13Z","title":"Neural Optimal Transport","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12220","snapshot_observed_at":"2026-08-07T15:24:50.219751Z","title":"Neural optimal transport","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.219751Z"},"links":{"cited_paper":"/paper/2201.12220","citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:62509707d3c33d66a619cee393736a9e85481123cab5dce48ed7f23d00cd7ecf","observation_id":"87db3f91-d9cc-4cc7-a5f6-a9eae1f60131","resolution":{"observed_at":"2026-08-07T15:24:50.219751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:57.272319Z","title":"Sigma: Semantic-complete graph matching for domain adaptive object detection","venue":null,"work_id":"37f21e79-0513-4aa8-805e-90ad4ed7739b","year":2022},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.300678Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:3fbf66883b97528e49d95a6bdf3b4a6885163e9ecb87aa36e9e9fd74c55b0445","observation_id":"dc23062f-e4eb-4b3d-98b4-c6d3750ce7d3","resolution":{"observed_at":"2026-08-07T15:24:57.367491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:50.393361Z","title":"Graph matching networks for learning the similarity of graph structured objects","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.393361Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:cd35c6a569ebffda50fd0fc55ab89a08b4f886437b9e5125151ee1810edc2336","observation_id":"53ed3991-a0ef-4e78-baae-e01aff02e114","resolution":{"observed_at":"2026-08-07T15:24:50.393361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:57.055879Z","title":"Multilevel graph matching networks for deep graph similarity learning","venue":null,"work_id":"1bea1ec3-f440-42e0-bd5a-a85278f79624","year":2021},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.498738Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:4f2bfcab0e4525c725a85c872edbd6cba838d9274e6ff57229a59035c1cd1cce","observation_id":"3c8c676a-721e-4375-b56b-672e726b885c","resolution":{"observed_at":"2026-08-07T15:24:57.155564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:56.920620Z","title":"Self-supervised learning of visual graph matching","venue":null,"work_id":"eb2102c5-48ca-4b00-a827-e52af845a93c","year":2022},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.617578Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:bbb0ed8131e9694537dd921ff087353d3cea342222e05130009890a0d8f1b144","observation_id":"532160fc-f92a-4106-b501-3c895400a160","resolution":{"observed_at":"2026-08-07T15:24:57.006274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:56.755702Z","title":"A survey for the quadratic assignment problem","venue":null,"work_id":"c64d43f2-c033-4429-90c0-7fc616644c02","year":2007},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.701809Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:557d95f2d91494490e43d696a244c8bc011bc59fbcce35333f0e3ed91073c3ae","observation_id":"f1d1022f-7211-4a66-bfbb-4c007133bfd0","resolution":{"observed_at":"2026-08-07T15:24:56.805848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:50.808101Z","title":"Gromov–wasserstein distances and the metric approach to object matching","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.808101Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:2afd9e6da22b196841d778d0e5cae33f911e86fb5a8f79bb301145ff4e07e292","observation_id":"48a030ec-c640-4205-9b55-5f95e4fed7bb","resolution":{"observed_at":"2026-08-07T15:24:50.808101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:56.581593Z","title":"Graph node matching for edit distance","venue":null,"work_id":"f1433b55-1e3e-49fc-9572-8c355b7b9f7a","year":2024},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:50.938427Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:add73fbe50b0b279478c3007ebc3c6bb7221447a8dc87328426596d257c6b540","observation_id":"b74a57d2-5551-4dda-8c4a-ed9416ece763","resolution":{"observed_at":"2026-08-07T15:24:56.666370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:56.409950Z","title":"Neural gromov-wasserstein optimal transport","venue":null,"work_id":"dd881623-8e7f-4f7e-b3ed-e03d9058112f","year":2023},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.023310Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:f3e1078ab139ffd4f60d41bc8fa5b47141c78a098e6fc5a271d89281282338c6","observation_id":"60c2f57f-2182-43c4-94cc-302d98f79f56","resolution":{"observed_at":"2026-08-07T15:24:56.501003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:56.270042Z","title":"Functional maps: a flexible representation of maps between shapes","venue":null,"work_id":"9ab83498-b821-498e-9634-c942ddf08812","year":2012},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.138814Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:758d59338ec43f529b3def7af6dae9255abe76968dfe29867a243dfd25ad9c66","observation_id":"73889b24-ff48-4c23-95f7-8bc5963bad3d","resolution":{"observed_at":"2026-08-07T15:24:56.319243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:56.120665Z","title":"Gromov-wasserstein averaging of kernel and distance matrices","venue":null,"work_id":"e7d55af7-e8b5-42c6-b7db-60a68aea5dde","year":2016},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.229813Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:7eed29ad3ae8454d5a4b2a394f4d3ed7c99c101744fda10b72db99369cd33978","observation_id":"08f2e4f2-8f3c-4ee5-8db0-1006a16755b3","resolution":{"observed_at":"2026-08-07T15:24:56.178421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:55.986738Z","title":"Computing graph edit distance via neural graph matching.Proceedings of the VLDB Endowment, 16(8):1817–1829, 2023","venue":null,"work_id":"ef91cc41-a85a-4313-9fca-93b00d0a0974","year":2023},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.311493Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:66e6a2a8c8bc228d621eb0ac07db3c1a9827338a61066ec91a420e4e768cadaa","observation_id":"9f5d8ff9-6249-473b-8f0b-90bf1da4dacf","resolution":{"observed_at":"2026-08-07T15:24:56.039296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:55.858789Z","title":"Individual brain charting, a high-resolution fmri dataset for cognitive mapping","venue":null,"work_id":"0da69e12-ff4f-46f1-a11c-f4661929e364","year":2018},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.415325Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:77e4d34f2dedf3630a433c6384c717adea40ccfa6f7a434620b923e4bd85ff89","observation_id":"109782ef-cfc5-4902-977d-14dde9ca0528","resolution":{"observed_at":"2026-08-07T15:24:55.933815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:55.726073Z","title":"Optimal transport for multi- source domain adaptation under target shift","venue":null,"work_id":"57cd8650-a067-4b2b-a7ac-b13506415c5f","year":2019},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.480977Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:57215003f10ccc7cf77ad682ba8c7286a3564d0739610bbe4858351361caaafe","observation_id":"8a5d70be-c7d1-482b-a9ad-b3068bf59a19","resolution":{"observed_at":"2026-08-07T15:24:55.783267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:51.535524Z","title":"Superglue: Learning feature matching with graph neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.535524Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:23308432660bde93866a2f0c515878add216015773662bbd470ffac09490c75b","observation_id":"04a33c9f-3553-4d30-a7fe-2dc5daaa4cb7","resolution":{"observed_at":"2026-08-07T15:24:51.535524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.02283","last_updated":"2018-02-26T01:25:22Z","snapshot_observed_at":"2026-07-06T06:08:05.574975Z","submitted_at":"2017-11-07T04:53:07Z","title":"Large-Scale Optimal Transport and Mapping Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.02283","snapshot_observed_at":"2026-08-07T15:24:51.635434Z","title":"Large-scale optimal transport and mapping estimation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.635434Z"},"links":{"cited_paper":"/paper/1711.02283","citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:1ac7b69e01f760abbd61035fd9ed4e33180aa6278fda0139c2b800393bad8571","observation_id":"d4d67a8c-48f9-4b31-8cbc-59ec3c455756","resolution":{"observed_at":"2026-08-07T15:24:51.635434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:55.597394Z","title":"The unbalanced gromov wasserstein distance: Conic formulation and relaxation","venue":null,"work_id":"8a8f066e-801f-4420-8e30-4fe93472bc62","year":2021},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.726001Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:8cd85ef929f810056b42a35914e6edb293d5a50d6d6bbea387f8803eed5e6069","observation_id":"93e449b4-64b2-4329-9df5-ddef157a5e45","resolution":{"observed_at":"2026-08-07T15:24:55.657751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:55.452101Z","title":"Wasserstein propa- gation for semi-supervised learning","venue":null,"work_id":"4160182d-0e66-4425-9f8f-a020aed0987d","year":2014},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.807505Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:4339441ce0d1e44ff449ce6adfba97f21c76bae2509d92db6514e4b8cbe90d61","observation_id":"72c83e5c-7106-4053-9983-be443bbfb943","resolution":{"observed_at":"2026-08-07T15:24:55.526406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:55.302544Z","title":"Which fmri clustering gives good brain parcellations? Frontiers in neuroscience, 8:167, 2014","venue":null,"work_id":"24761f2c-cd28-43d3-8b8a-8dabc15e2faa","year":2014},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:51.872377Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:542c4dfa234b9db75bcf4364f2b4b1de496698b2c4db687837fac191eb23f12f","observation_id":"f6c07377-dd57-415a-b30d-928a218c3fd2","resolution":{"observed_at":"2026-08-07T15:24:55.368592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:55.127016Z","title":"Aligning individual brains with fused unbalanced gromov wasserstein","venue":null,"work_id":"41e6a633-0456-4873-979a-df63f6d6ff07","year":2022},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.002238Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:cb4607ce08df610911eb940b60332ab3c781d87366f48ebd83ebee4b532fc9f0","observation_id":"6bcdfe4b-7c7c-4f6f-bea8-990123c63a39","resolution":{"observed_at":"2026-08-07T15:24:55.201379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:54.993268Z","title":"Optimal transport for structured data with application on graphs","venue":null,"work_id":"a5dbc16f-6c1b-4610-9230-0819b43c9e32","year":2019},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.094532Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:205aabca4f922e8408dc6da1cd2f0a7cea057e640a36b0c714f4e3d6faecf67c","observation_id":"9139461f-2e4d-4cf4-a282-7e4d97fdb892","resolution":{"observed_at":"2026-08-07T15:24:55.051719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.00482","last_updated":"2024-03-11T14:27:48Z","snapshot_observed_at":"2026-07-06T14:47:04.817434Z","submitted_at":"2023-02-01T14:47:17Z","title":"Improving and generalizing flow-based generative models with minibatch optimal transport","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.00482","snapshot_observed_at":"2026-08-07T15:24:52.188079Z","title":"Improving and generalizing flow-based generative models with minibatch optimal transport","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.188079Z"},"links":{"cited_paper":"/paper/2302.00482","citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:b05bac5e8e660cf28a03a7814cd79007386535cf00b1814049bacdbdc9be276c","observation_id":"3680a61c-7c76-410d-ac65-43e188369151","resolution":{"observed_at":"2026-08-07T15:24:52.188079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:54.814336Z","title":"Discrete cycle-consistency based unsupervised deep graph matching","venue":null,"work_id":"ddfa47d5-fea5-4e54-bb1d-61890b7d664b","year":2024},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.296738Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:4d445a8771974e9d6ff4dd589fefbe0f4b7c2997abc444bf92ab38b7a869aee0","observation_id":"6591099d-93a5-4bba-ab0e-27bdf3ad6048","resolution":{"observed_at":"2026-08-07T15:24:54.892542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:54.675331Z","title":"Fused gromov-wasserstein distance for structured objects","venue":null,"work_id":"9eeb0da3-58a3-407f-abb7-e244defaa733","year":2020},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.388991Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:bb3e56096d1460a763830115872c3812ea57f9e2b2268659b4777d63072960f5","observation_id":"e1d1938b-1df9-45fd-9359-ad04f618277e","resolution":{"observed_at":"2026-08-07T15:24:54.724052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:54.517149Z","title":"Deep learning of partial graph matching via differentiable top-k","venue":null,"work_id":"3657e7ad-a779-48a2-9afa-6172ffa601cd","year":2023},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.493747Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:2664a199601026bb3ebb7daeba1fdb425aa4c6fe58b3d4bc097d784021738cf2","observation_id":"7a98b4c9-7f54-45b9-8304-6f964fbb7055","resolution":{"observed_at":"2026-08-07T15:24:54.584058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:54.381082Z","title":"Learning combinatorial embedding networks for deep graph matching","venue":null,"work_id":"eafa9a39-a296-40cd-a6cc-db6bdfa1f082","year":2019},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.581856Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:9680cd2de7079e7ea574b75eda95f7151935c4095d539f5cdd4b2e27554a9c3c","observation_id":"e4faead0-7d44-4aaa-b446-b64b72987ecc","resolution":{"observed_at":"2026-08-07T15:24:54.429986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:54.221748Z","title":"Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning","venue":null,"work_id":"a3505de8-2866-4b22-9b54-7c0935609daa","year":2020},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.658492Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:4f069862ba6e29950f6b48745d5f3ba9c0601001b6fd9fe0234ad19da9f4a5df","observation_id":"2ef6f582-a8ed-4ffc-9d26-8ecfd47c24a4","resolution":{"observed_at":"2026-08-07T15:24:54.293481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2312.07397","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:53.624745Z","title":"Neural entropic gromov-wasserstein alignment","venue":null,"work_id":"45974bd6-1d54-45f5-be2a-81537ad2108c","year":2023},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.748212Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:0acb05198d55bea69820cb702718fc19429a4f5564caa65565d6af745fe84bbd","observation_id":"df7511df-f430-43c9-bb0f-d425bbb55a7b","resolution":{"observed_at":"2026-08-07T15:24:53.698378Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:54.078148Z","title":"A fast proximal point method for computing exact wasserstein distance","venue":null,"work_id":"65b9d798-9eaf-47f7-8abc-e82463f1afd7","year":2020},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.846597Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:390f3382d7fc30ad16c749647fa8a67610d3a36c2fdb84566b3c322de1a88bcc","observation_id":"98d0be43-04a9-4a31-8836-dcef4713fb56","resolution":{"observed_at":"2026-08-07T15:24:54.133623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:52.911069Z","title":"Gromov-wasserstein learning for graph matching and node embedding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.911069Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:86f267e38a3a604736134d68d0a6a398dfa842b0e5a77d8cefc296b3813ea17c","observation_id":"be0d4955-2619-4569-a599-2b5076a9b983","resolution":{"observed_at":"2026-08-07T15:24:52.911069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:53.882946Z","title":"Deep learning of graph matching","venue":null,"work_id":"9d1959c0-90a3-4572-9b01-f47dd8b167b6","year":2018},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:52.986799Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:8ef753e800481185971c3808fab380f4ebbb099f882c6d367fd4d0eb51208cd0","observation_id":"ee2e2558-7355-4766-85a0-31119a9dabfa","resolution":{"observed_at":"2026-08-07T15:24:53.962584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0000.1590","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:24:53.377816Z","title":"Gromov– wasserstein distances: Entropic regularization, duality and sample complexity","venue":null,"work_id":"16a71412-4544-4820-b862-f98c45941a81","year":2024},"citing_paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:53.081551Z"},"links":{"citing_paper":"/paper/2506.12025"},"observation_digest":"sha256:94e15ac6d18ab1254fdbc86b7dcb4f635d10fe418fc3c52c678ec5cb5b353d14","observation_id":"55dec0cf-bcbe-4997-99ac-9cff091b4ef4","resolution":{"observed_at":"2026-08-07T15:24:53.434382Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.12025","last_updated":"2025-07-08T11:47:25Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T15:18:23.431192Z","submitted_at":"2025-05-21T09:29:19Z","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":2,"verified_fuzzy":30},"total_outbound_references":42},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"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."}