{"as_of":"2026-08-09T22:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0b37e051ee587bf822657d322a4c0bb2b2976f0d47858ab100466a13ee9dc35f","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T09:45:28.800481Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.28488/citation-record","integrity":"/paper/2605.28488/integrity","json":"/paper/2605.28488/citation-record.json","paper":"/paper/2605.28488"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"A note on the relations between mixture models, maximum- likelihood and entropic optimal transport, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:29be1e85402912fb973f0016603f2f9fd110fee9fe209019bd682d48f43c92b2","observation_id":"fff73c56-8138-4637-bc50-f8e9678f90b1","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":"2018.00361","doi":"10.1109/cvpr.2018.00361","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Rohde, and Heiko Hoffmann","venue":null,"work_id":"ce0bd673-b8fa-4fea-9b7e-ea4d710affbb","year":2018},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:d3c2973e3f82a8c61c3bd64817ac5621c660673718147315b4d8828d99c2cb30","observation_id":"92e32da7-6ed2-4ab0-a39d-6bea53695b06","resolution":{"observed_at":"2026-06-29T09:53:16.965856Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Entropic optimal transport is maximum-likelihood deconvolution, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:13bc848e5086c64f6e8f9e9a980e6aeda27cc9171525c66c336dc18037cb05ad","observation_id":"6d4da8fb-bf38-480d-9bbd-50a6b0d7fe81","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Stochastic blockmodels: First steps.Social networks, 5(2):109–137, 1983","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:d1416f82d86269ae9b53ce4adc04df8257cabaf16a9fba3008bd873dfe7c1d88","observation_id":"c61f8b59-2d2e-429e-9889-65c802264381","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Estimation and prediction for stochastic blockstruc- tures.Journal of the American statistical association, 96(455):1077–1087, 2001","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:bfe321d95a1275de89fc87831c3cafd495deb96342fefd08c030f0719517f866","observation_id":"65901bcf-0db7-40e9-bb11-25b5f9081fd4","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":"10.1007/s11222-007-9046-7","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A mixture model for random graphs","venue":"Statistics and Computing","work_id":"84eaf7c7-c7a0-4053-b6d2-8911e1154393","year":2008},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:760f87da5cf0c4e68526447be9e2560e1108df31dbf5620155ec7d5983876a9a","observation_id":"533a7222-0357-4389-bb7a-27be105dce65","resolution":{"observed_at":"2026-06-29T09:53:16.960499Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Network analysis in the social sciences.science, 323(5916):892–895, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:5c42b959b1acd47109dcbf1b3d1a02fc36e7415b33612fa8dce8698e903df20d","observation_id":"a9b2e534-b1fc-40ad-aff7-eb5b93ea76ff","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":"10.1088/1742-5468/2008/","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T12:34:38.295421Z","title":"Journal of Statistical Mechanics: Theory and Experiment2008(10), 10008 (2008) https://doi.org/10.1088/1742-5468/2008/ 10/P10008","venue":null,"work_id":"d47e4108-d479-4db4-9c61-75a5a31d6a1f","year":2008},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:4f46d41bd9780fe947ed4bfc34fb148d760bd1ff7a1ab53b2f8f1763b7ad88a2","observation_id":"131db4e8-c885-44b8-96be-1389c64f1e42","resolution":{"observed_at":"2026-06-29T09:53:16.972247Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"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":"2026-06-29T09:45:28.800481Z","title":"Community detection in graphs.Physics reports, 486(3-5):75–174, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:6f01c859ccea2ee6f79521269e66b353dbee05747445a6e188046dcb8621b412","observation_id":"a89d4a73-262a-4703-bfb4-42fd8144f4fd","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"A tutorial on spectral clustering.Statistics and computing, 17(4):395–416, 2007","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:e93d0da6a431bf9db94e1e460893eac91bfd18e42a2c2241d8c1d47df4f58a94","observation_id":"985a1154-443c-43c7-b0a7-f5c0231cc21d","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":"10.1214/14-aos1274","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Consistency of spectr al clustering in stochastic block models","venue":"The Annals of Statistics","work_id":"afe75974-7e4e-4d6d-bfeb-7bfece5af9dd","year":2015},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:62e9df7cd1746629e7e957f0db795cfb536162c3ebc52df3ffcb4f6bf5fd5521","observation_id":"bd188c2d-9fce-4473-8cfb-e745e4a025f3","resolution":{"observed_at":"2026-06-29T09:53:16.962124Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Mean-field theory of graph neural networks in graph partitioning.Advances in neural information processing systems, 31, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:cc061b290e456fdaef1c4ec13a33a3e6b5ed5b028e85f288ec1346efdea3c649","observation_id":"886a4b9f-4d30-4bdd-89b4-ebd455d08b50","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Neurocut: A neural approach for robust graph partitioning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:dc8c8b65f14a6b63a7733ed87831a88acff896a2daae64c47ac1e4b06381f70c","observation_id":"838720ad-16ad-4c53-b3b4-eb53e0e2f42a","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Scalable gromov-wasserstein learning for graph partitioning and matching.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:e3eecfcc431a71719e1d69e2395078f08658efcc6c1113e4ec89e5454433dbef","observation_id":"6da4db6d-3945-4531-bf47-677f34c547f9","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.02753","last_updated":"2022-03-01T16:28:47Z","snapshot_observed_at":"2026-07-06T11:54:57.307811Z","submitted_at":"2021-10-06T13:38:31Z","title":"Semi-relaxed Gromov-Wasserstein divergence with applications on graphs","version":3},"cited_work":{"arxiv_id":"2110.02753","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.02753","snapshot_observed_at":"2026-06-29T10:03:17.342613Z","title":"Semi- relaxed gromov wasserstein divergence with applications on graphs.CoRR, abs/2110.02753, 2021","venue":null,"work_id":"7a072a49-cb18-49de-9b97-e3bd325878d7","year":2021},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"cited_paper":"/paper/2110.02753","citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:e849fa6339883878d91ebc92253ca239bd2d810732e999e9719c90012bdbfde6","observation_id":"8163311f-9a9a-47e7-a857-0d14ff59ef8d","resolution":{"observed_at":"2026-06-29T10:03:17.344495Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Heat kernel based community detection","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:a582d3973b784edf91510e44334070ae2552b95ed48cfc8961e6d1b472e7ac7a","observation_id":"4438966a-1652-4754-9259-4f6d3072c3ad","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Stochastic blockmodels and community structure in networks.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 83(1):016107, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:c658502e04e8b7c4c79ab962bf1c2a1ee0a9337e4737fb6ddc0bf2af59120183","observation_id":"7731d357-2f46-4a71-bbfb-0a7b562438f3","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Assessing a mixture model for clustering with the integrated completed likelihood.IEEE transactions on pattern analysis and machine intelligence, 22(7):719–725, 2000","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:ac78534947eab1edce2dbc25558061a6e2baf891a51506467fbb4844ee656a42","observation_id":"317dac7e-230c-4ec2-9fae-855bba67f1e4","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Improved bayesian inference for the stochastic block model with application to large networks.Computational Statistics & Data Analysis, 60:12–31, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:f595e35cd115fcd0d229171c26a6eda810e8569f8e2afc2d5168e5c2ebfc6e96","observation_id":"8d978787-08d9-4a3e-b461-77ad2c54d030","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":"10.1007/s11634-021-00440-z","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Hierarchical clustering with discrete latent variable models and the integrated classification likelihood.Advances in Data Analysis and Classification, 15(4):957–986, 2021","venue":"Advances in Data Analysis and Classification","work_id":"eba79361-a7f1-47c2-869a-307cdd79223b","year":2021},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:7fb5d64e251fc7d62a3be09549c19b7248cdf56112f7f8d8962937e215111e9b","observation_id":"31907dca-a87d-4b59-b6ea-c9067d29f467","resolution":{"observed_at":"2026-06-29T09:53:16.971989Z","resolver_source":"doi","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":"10.1214/15-aos1354","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Annals of Statistics43(6), 2624– 2652 (2015) https://doi.org/10.1214/15-AOS1354","venue":"The Annals of Statistics","work_id":"94dc8b1c-f241-4b67-8449-240d8e3b74bd","year":2015},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:215905663ae86467018e4cfb39f427230abb5305daa368b789dd7006cb576338","observation_id":"a091c50c-d225-41af-86d1-5daa72da2647","resolution":{"observed_at":"2026-06-29T09:53:16.970068Z","resolver_source":"doi","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":"10.1016/j.jspi.2021.04.003","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Maximum likelihood estimation of sparse networks with missing observations.Journal of Statistical Planning and Inference, 215:299–329, 2021","venue":"Journal of Statistical Planning and Inference","work_id":"a2269f9f-4fa3-434a-a691-bdd63c7f132c","year":2021},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:614acbd215afee62dbe4747f89d4356d5eeb788acdff46003f9e0f00576ab171","observation_id":"d5cc6aa6-84a1-46f5-8457-612902e930d5","resolution":{"observed_at":"2026-06-29T09:53:16.967696Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Optimality of variational inference for stochasticblock model with missing links","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:67fa21e7d8a3bbf4c282ffd1e7600f032a8b5ae928aa8df445ca2485f96d3e68","observation_id":"9f1026e6-cfe1-420b-baac-d41219202437","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Spectral clustering of graphs with the bethe hessian, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:1d1a4f2da7d37f12c7c75382283b11302347fdb54e90e7d5b8a6333a66e84598","observation_id":"085f8dda-e89c-4ad4-9297-4c9fbb59a1c2","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Recovering communities in the general stochastic block model without knowing the parameters, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:13697a07b5a7e628001faa4443ac20e089e375de1bfe58295c0f4a2eeda6c04a","observation_id":"7cb27795-135e-4de8-9585-387dd660e1df","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Grundlehren der mathematis- chen Wissenschaften","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:25909255b7b9e6fda34103e728e2d8fa18038ed3748b728ee64e504ec8844f15","observation_id":"d480e89e-5d67-4443-8631-28d66c1d8019","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Computational optimal transport, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:39249deecf4b48a2b6ddac5a0bb03a559a10652c6b18301641dd0b221647b60d","observation_id":"717b809d-8c08-428d-ba80-52559b6ed045","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"A survey on optimal transport for machine learning: Theory and applications.IEEE Access, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:c7670dccf46553ba0cda8561345a1835c3a0097e41f9d34f719a4733fe2f73fa","observation_id":"f858e513-ae67-415e-b1b4-8618c39606c2","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Gromov—wasserstein distances and the metric approach to object matching","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:0289d193760b04e680f0e2efdb1b450c6ad4d0ff770b51c370c04472f9cdf5e2","observation_id":"9b026dc0-b245-471d-9815-c19c73918edc","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"American Mathematical Society, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:804dd509bf49916deee8f1973f72bd0b965c2bf90d3f39cdca7a38c881530d9f","observation_id":"721ab98c-e5e5-4068-9a07-4699431ca6a4","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"The gromov–wasserstein distance between networks and stable network invariants.Information and Inference: A Journal of the IMA, 8(4):757–787, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:771cf19dc5008f31c6754b8ab63b3d727bbff86a8d7ecd2e53536bbfe74f2d59","observation_id":"8df705fe-be76-44c2-90c2-3da6430ddda0","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Gromov-wasserstein learning for graph matching and node embedding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:09d3b11b77b4171d122e0d95d3c421d5dbc76a43861716be6682341fd10a7c05","observation_id":"c51f2c53-6c3e-46f3-b1f0-4b612acdebc7","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Quantized gromov-wasserstein","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:e576633065557cf50708d0bdc14c9175cceb4ed145d50db4907ec961b7adf5c4","observation_id":"4fd21264-b4cf-4072-ad33-92a164d6e234","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Gromov-Wasserstein Averaging of Kernel and Distance Matrices","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:ee0d12b223c40f9bba22e119a348ece17dc5f10fd288e2db4a7b2c5ed35cec10","observation_id":"1255bca4-4017-4991-96c9-dca48b2f9ab8","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Optimal transport for structured data with application on graphs","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:76812b5d78ad3fa697cbe88d30d135b0f8ec665bfaa23d42e6635e01ad62f988","observation_id":"589c5105-ab4f-436b-9b90-79ad9847aff8","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Generalized spectral clustering via gromov-wasserstein learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:5276c7e86edc99634c8662dd3f4df2cefc2e3868d39c1a5d661498cdf63a65a7","observation_id":"8f07cb1d-51c4-4ebe-845b-b95130449905","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Optimal transport-based cluster- ing of attributed graphs with an application to road traffic data, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:8c5a32334dc858c1c4af3031299e0184663482654f2ca527ba92085c191e6c57","observation_id":"cbce82a4-a1ea-44c0-ab6a-ed085afc31e9","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Learning graphons via struc- tured gromov-wasserstein barycenters","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:8157eb547defc531437a198a914abf33106b2b4ca4884f1a85e5f8390ee60f6f","observation_id":"54fd5f6a-3663-41cc-8f5c-322415acf3a0","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"A gromov-wasserstein geometric view of spectrum-preserving graph coarsening","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:58e73c7ed27b3e0941aae6388115bd26c3a30074c967c7620c102bfaa17e7544","observation_id":"0b159da3-f2ac-4e15-a515-a6b7ebffb65c","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Gromov-wasserstein factorization models for graph clustering","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:0eb0b722362cbf894cc55ba61791050c7ced37323ce2c6a23c011ec34f14037d","observation_id":"171adcd3-c863-40ec-85a6-4c68d2b244d7","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Online graph dictionary learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:82e4267cfaa71cdc17f3b41f81b7efbf06b3e2d4c18484f3279b916ecdc8a76d","observation_id":"34dbd5f4-9f40-449c-8484-99e5be5f22d3","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Robust graph dictionary learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:47ba134c232ca13aafa5d32d78b2bd5dfd0123aa8b0129fd00c3e12e1779f7be","observation_id":"f5d316ce-8530-4b06-a45a-a7635d7053ac","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Generative graph dictionary learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:a58f74413393731c905f8c98e7aecafdb97030d39b24703384b4cabec453ee69","observation_id":"a7fbe284-6686-43fb-8d4b-dd8f782d89c4","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Template based graph neural network with optimal transport distances.Advances in Neural Information Processing Systems, 35:11800–11814, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:5479f699c5cdc47c68bb38ab152495affdc1121f9b975c56a61f0a5a5777b471","observation_id":"e0e7628d-9827-4e5e-8420-489049e4d978","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Wasserstein barycenter matching for graph size generalization of message passing neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:6ba185ebfe6db47369df84ca2431e6977084bb7ac1a9aad55cfcf9efb419b600","observation_id":"e6c949e0-2368-4cfe-bd4a-18b7ad3dcd3a","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Reimagining graph classification from a prototype view with optimal transport: Algorithm and theorem","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:58baa10e1f31d2412c1a8bc29d767c0d6fff801c83e358c1603934f16cacd43d","observation_id":"5b2227e9-0b39-4d56-925e-4d69e164e009","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"The quest for the GRAph level autoencoder (GRALE)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:4c1852c500e9fd52eabc4690696f38d17d3980821dfd7eeb4a51e2b7b25fbfbe","observation_id":"c92c8c8d-fcf8-42ab-bc79-1e3ce506d406","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Distributional reduction: Unifying dimensionality reduction and clustering with gromov-wasserstein.Transactions on Machine Learning Research Journal, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:54e6161a3db094ca5474442bb9a588393deadca70acdf8ec981bd7a5fdb52f58","observation_id":"ad000501-85ae-4bd6-a585-42f5ab16745d","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Generalized dimension reduction using semi-relaxed gromov-wasserstein distance","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:fd6af7042c01c9791f8ae551e7fdc9b4785e0342e157b2c2f740ab3fbbd6e01c","observation_id":"c7384b65-ac6c-4bfb-b675-6254654e5a16","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Kernel k-means: spectral clustering and normalized cuts","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:6477c96d4bb9b21ccf5dd8c84ca288c27e7b7422c9f5432d5595ad591b4a7d73","observation_id":"75d9086b-0e55-4693-a1fc-9ddce2ca8380","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"A review of stochastic block models and extensions for graph clustering.Applied Network Science, 4(1):122, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:445588205eeced7cbdf3b6a345ce24ddce430abb9a80adf48b22b6e4a45121b1","observation_id":"74d75535-6fb4-49ce-8117-daeca5df7def","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Daudin, and Laurent Pierre","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:edd4cb0fcd3ef3e0775457bd3941aa4f9307fbf0c2d1cb6539c9cbff0244dd30","observation_id":"b266333f-241e-431b-8088-1f9a90474647","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Convergence of the groups posterior distribution in latent or stochastic block models.Bernoulli, 21(1):537–573, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:843eab0d5a5dee141e31c28fcad04036c0b67cf36f28c61ef379448cb3645899","observation_id":"78a418f3-d6f6-4544-9791-83747349c853","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"A graph matching approach to balanced data sub- sampling for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:e3fc0f3ecf488827991836df69048ca243837d3e1330a0cdd92d57e182268410","observation_id":"94106d64-28ba-476b-83e4-6ced732fa4b4","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Itera- tive bregman projections for regularized transportation problems.SIAM Journal on Scientific Computing, 37(2):A1111–A1138, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:a7ba97b5e0d1541465670b87350db87385ca22a3bf6c8a8f9790942138e5869d","observation_id":"d0727094-917c-429d-9975-0570b93bc630","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"The map equation.The European Physical Journal Special Topics, 178(1):13–23, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:f57d669d79ceed183b71b446c59d90b46e722ebcab7f62963780aa4261e459b0","observation_id":"24ebd4c9-4661-4d62-8269-55af54ef06bb","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Blockmodels: A r-package for estimating in latent block model and stochastic block model, with various probability functions, with or without covariates, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:bb2e401ba5ef22f8983f85af346f714198543fecc080393e249be37cb32ce129","observation_id":"e44a24e1-0b8f-4d7d-aa16-f7566c44c500","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Adjusting for chance clustering comparison measures","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:43c5c9d146da2f4705869158d00a2ebe11e837df6baa7f7e14bba56913a10434","observation_id":"7bd63b47-1529-4a47-880c-b41b8a2fe667","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Comparing partitions.J","venue":null,"work_id":null,"year":1985},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:4b8454aba26387775f759c31450b50613b1d1754f0f4fc0d2bbeb4de8f3d0ec2","observation_id":"9919ef64-ce20-4bdb-ae5b-12646454ef80","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Cambridge Series in Statistical and Probabilistic Mathematics","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:5d404d16b2ceab19327195ffcaa9d016d8f5d61b55e762516bdee77990e7929b","observation_id":"be3af533-d863-4895-9076-cb84acacfb75","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Asymptotic statistics (1998)","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:8da90a59433848bbb187ddf5194e0c52f162c6a13c7adefa01a73fe9ef9a9099","observation_id":"1ae6b539-9938-4acd-88f1-c9ed1f5c5fbf","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Pot python optimal transport (version 0.9.5), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:28e72f05701d78b0c8a8ef29d6882a2c8b17d1d92ff2986a9daf1382015e5515","observation_id":"3a7ae472-4ce3-42c9-9a94-e1cafa84d482","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:6820df18bf9b34df523840f4a3d9ee29fa25c0db2a2cec5ff7075f18ef06724e","observation_id":"c8052520-7c9c-4632-9c69-c72c35952447","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"projection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:193c2fc6c39f2fb73b4f032bb67092201f55edca47903933841ce6ec2e97d17e","observation_id":"fc3cd6aa-2499-4ebb-9a5c-2713133ce28e","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:45:28.800481Z","title":"Justification: The research does not involve human subjects, therefore IRB approval is not applicable","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-29T09:45:28.800481Z"},"links":{"citing_paper":"/paper/2605.28488"},"observation_digest":"sha256:480a569a3f75ab2dfcbe3d2afdcceffc42eb560585b6359b0739fa73e084553c","observation_id":"0096bcca-eaae-4142-9e60-0a216b8a9712","resolution":{"observed_at":"2026-06-29T09:45:28.800481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.28488","last_updated":"2026-05-28T08:14:25Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-06T22:35:47.751030Z","submitted_at":"2026-05-27T13:44:43Z","title":"Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":56,"verified_exact":7,"verified_fuzzy":0},"total_outbound_references":65},"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 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2605.28488."}