{"as_of":"2026-08-18T11:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a7ce85a063daefbb96e0c15a552960225e6d7a2df6bcf3e7053007c456b81a6a","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:07:19.106455Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2411.11020/citation-record","integrity":"/paper/2411.11020/integrity","json":"/paper/2411.11020/citation-record.json","paper":"/paper/2411.11020"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:07:19.787070Z","title":"Adversarial label-flipping attack and defense for graph neural net- works,","venue":null,"work_id":"9c1f18cd-a872-4636-8e57-3b69f4361258","year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.881953Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:01b6923348cace4ddbb0a112f549f536cc82a9ef5621f68a84ff014a40d47237","observation_id":"f8523b30-11fd-4a6b-beb3-e084fae2e357","resolution":{"observed_at":"2026-08-12T19:07:19.791681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.773758Z","title":"Inductive rep- resentation learning on large graphs,","venue":null,"work_id":"94c17f2e-6fba-4300-bac6-3da1012d59b9","year":2017},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.886523Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:f26712ac5e763a3d4a9395ff1a634aa0b230c60e72ddd817f9ef7e38cf7e0cc4","observation_id":"f8a1a77a-4d18-4c3c-ac45-41cb9cc3c42e","resolution":{"observed_at":"2026-08-12T19:07:19.777653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.761781Z","title":"Modeling network- level traffic flow transitions on sparse data,","venue":null,"work_id":"5c707ee7-b845-4410-a978-0f230f6fdb53","year":2022},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.891091Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:fa695a8820929b74249dcc64b7f0ac62fcaf4d20e91ebc00b3968e53cb60ae7e","observation_id":"fcba64ff-8579-427a-bd89-1b60edcd1cd8","resolution":{"observed_at":"2026-08-12T19:07:19.765822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.748367Z","title":"Stochastic weight completion for road networks using graph con- volutional networks,","venue":null,"work_id":"22c25f7e-d523-4773-a391-013d0bff25a9","year":2019},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.895490Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:12609a2e5ed63ab0c4dbb27d1d25d6e537b58b4dd590d5bee41bad233e9af685","observation_id":"29826df2-63dc-4c91-8ae8-172dcb0eefef","resolution":{"observed_at":"2026-08-12T19:07:19.753201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.735063Z","title":"An edge feature aware heterogeneous graph neural net- work model to support tax evasion detection,","venue":null,"work_id":"d65d0246-d47a-432f-be1c-9ee780098b76","year":2023},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.900035Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:eca016a15922eb3c630b1547747034d6620edec2875613034f026d14281fd9fb","observation_id":"694b5f65-b936-42fa-a713-99a0acf7c284","resolution":{"observed_at":"2026-08-12T19:07:19.739083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.722543Z","title":"Tax evasion detection with fbne-pu algorithm based on pncgcn and pu learning,","venue":null,"work_id":"8d0a394a-40c2-446d-a3cb-17aa3c2ebeeb","year":2021},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.904088Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:b7624d822d6d96af19ecbd159147099c586dbd78f9bd339190fb138baaf92a03","observation_id":"bb920f57-5589-4c4f-b223-2eb5a8520291","resolution":{"observed_at":"2026-08-12T19:07:19.726473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6203","last_updated":"2014-05-21T16:27:09Z","snapshot_observed_at":"2026-08-14T23:50:42.645895Z","submitted_at":"2013-12-21T04:25:53Z","title":"Spectral Networks and Locally Connected Networks on Graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6203","snapshot_observed_at":"2026-08-12T19:07:18.908220Z","title":"Spectral networks and locally connected networks on graphs,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.908220Z"},"links":{"cited_paper":"/paper/1312.6203","citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:231dcd207cf6a912cb25e8f0857347015cc42f8933a96270699ee414ab1b42f1","observation_id":"49579d8b-d78c-4abf-91b2-d05fea8486fb","resolution":{"observed_at":"2026-08-12T19:07:18.908220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-12T19:07:18.912783Z","title":"Semi-supervised classifica- tion with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.912783Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:fb3afe596a0f9d631cf5d2e2c3ffed96862b68667f5314ae500e0ea2fd9ced79","observation_id":"7a40941f-9d24-4fe9-88b6-d6b699ecf10a","resolution":{"observed_at":"2026-08-12T19:07:18.912783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-08-13T05:06:48.606308Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-08-12T19:07:18.917506Z","title":"How powerful are graph neural networks?","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.917506Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:c0263fad8e0e1232f10e75f0743d0f298cb746a4f4c6986760a4833c4213972b","observation_id":"688ecc07-f346-4962-933a-1f7d48178def","resolution":{"observed_at":"2026-08-12T19:07:18.917506Z","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":"2026-08-12T19:07:19.709133Z","title":"Graph based semi- supervised learning with convolution neural networks to classify crisis related tweets,","venue":null,"work_id":"7d743489-a2e2-4f6d-ae37-89c944cee721","year":2018},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.921961Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:f50ad97faad977eeeb1f5b56542f5e4ba8c57af5ddfbcb51479ac0acf9b424c2","observation_id":"1003c706-f4f4-4356-8323-ce40062f4e83","resolution":{"observed_at":"2026-08-12T19:07:19.714324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.697435Z","title":"Grale: Designing networks for graph learning,","venue":null,"work_id":"5e62cf53-b9a1-41e7-928b-97c8cff02e8d","year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.926187Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:92683fdfa9e9a15124972a1a2b4fb6bec55ea73229be6673fda684dc4b77dc14","observation_id":"004de759-8081-4130-9639-16318b14ab37","resolution":{"observed_at":"2026-08-12T19:07:19.701514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.684433Z","title":"Data augmentation for graph neural networks,","venue":null,"work_id":"e431cf4a-b955-4719-88b9-0daad4cd90d4","year":2021},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.930213Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:a7d0b6bd58443bffb66a34fa7a34b90db515cc54fa2ecfea52f122ec800ca444","observation_id":"93cc1e97-bfdf-4b03-b13e-679a143d159c","resolution":{"observed_at":"2026-08-12T19:07:19.688980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.671411Z","title":"Hard sample aware network for contrastive deep graph clustering,","venue":null,"work_id":"2efa59ba-4c14-436d-8558-a57fee6eaf1b","year":2023},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.934195Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:603496907f5c09e28b3ebc94add64c88de6c3215d1e5157946e3d7bc99d091c0","observation_id":"c7f8e43c-704e-47a1-a6e0-c868d8a8211c","resolution":{"observed_at":"2026-08-12T19:07:19.675828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.658194Z","title":"Deep graph clustering via dual correlation reduction,","venue":null,"work_id":"abf55696-4390-41c8-b939-dc15b22768ee","year":2022},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.937752Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:301502da44470255c276fbf306f69e072ba8baed78715837b8402d352667e6e8","observation_id":"496020c7-32b5-4dad-aeda-5e184dd52321","resolution":{"observed_at":"2026-08-12T19:07:19.663236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.642001Z","title":"Cldg: Contrastive learning on dynamic graphs,","venue":null,"work_id":"6c8b9a5e-3da6-4820-a306-7da5f7d27f0f","year":2023},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.941794Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:c9ce6473d807042ec4cbb693c9563b18b3695e3e7ac46049f8375c641eecd918","observation_id":"cf95fa95-11d8-45ed-8504-da78e24a2e8c","resolution":{"observed_at":"2026-08-12T19:07:19.647360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.629803Z","title":"Nrgnn: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs,","venue":null,"work_id":"9b12f8a1-1fa9-4084-b895-67236b625d8c","year":2021},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.946557Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:10b9f2d666d59ae7e87472ea84844615dae9a4906a0875df40ef70ef4470e4fc","observation_id":"2b874ab5-a49d-432d-b676-757da23e1bdb","resolution":{"observed_at":"2026-08-12T19:07:19.634142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.617233Z","title":"Robust triple-matrix-recovery-based auto- weighted label propagation for classification,","venue":null,"work_id":"b83a6adb-dfce-4f58-bb18-371d9b91ab42","year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.952887Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:1b58e337b8fbb0796563e691d77fa404fc43b4795d28daa55f4ffcb0ef7c0b01","observation_id":"ae9a52ce-4221-4b53-b9df-c7ffec7f0e2f","resolution":{"observed_at":"2026-08-12T19:07:19.621867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:18.956581Z","title":"Robust training of graph neural networks via noise governance,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.956581Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:9436f5798201d8828d886195b7141d08af36be2c9e7d975e6f685e5629d8232f","observation_id":"c3b1621b-7931-431a-b1b3-8aa3ee1636df","resolution":{"observed_at":"2026-08-12T19:07:18.956581Z","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":"2026-08-12T19:07:19.595526Z","title":"Gnn cleaner: Label cleaner for graph structured data,","venue":null,"work_id":"42916753-ab32-4733-b911-d20324842070","year":2023},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.960797Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:235ea24699f6789423ffd76703171f4bc0518a15a8ac3ee424ba99fc6046ebbc","observation_id":"bc8ab341-7386-4d3e-bdf7-824f9d0aed91","resolution":{"observed_at":"2026-08-12T19:07:19.600124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.580568Z","title":"Learning on graphs under label noise,","venue":null,"work_id":"461fc9d6-3e9a-4337-a5ec-6196389d3c28","year":2023},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.964478Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:37ee0b8575ad52b20f35ae95d6bd2451f97ad639f8ffe7f541b3317b793209a7","observation_id":"7d8f9938-79f1-4f39-9b42-e8fe7d87dcad","resolution":{"observed_at":"2026-08-12T19:07:19.585931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.07394","last_updated":"2020-02-18T06:20:06Z","snapshot_observed_at":"2026-08-17T14:10:48.435331Z","submitted_at":"2020-02-18T06:20:06Z","title":"DivideMix: Learning with Noisy Labels as Semi-supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.07394","snapshot_observed_at":"2026-08-12T19:07:18.968315Z","title":"Dividemix: Learning with noisy labels as semi-supervised learning,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.968315Z"},"links":{"cited_paper":"/paper/2002.07394","citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:665742df53747307dfd5f9a7f3658785c73326204e2b7ca97e69809d420fab93","observation_id":"dd476c99-a063-4cca-b05b-e0681662649f","resolution":{"observed_at":"2026-08-12T19:07:18.968315Z","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":"2026-08-12T19:07:19.562240Z","title":"Deep self-learning from noisy labels,","venue":null,"work_id":"5310fcc4-2e76-4752-ad7b-1e815942ffcf","year":2019},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.974067Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:665f466f3e66103a462e75ef32e1529f6eecbc2dcbb663d315babdd969185248","observation_id":"3886b31b-e133-4d5f-a515-ee9b03658e14","resolution":{"observed_at":"2026-08-12T19:07:19.567131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.545255Z","title":"Joint optimization framework for learning with noisy labels,","venue":null,"work_id":"695a20cf-10ed-4b2b-a9ef-ee85132bf456","year":2018},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.978392Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:4ff7590e76d3c527b104881d317dd25065a24a62e9846bfdd5172d3c39991d7c","observation_id":"8310ecb2-c637-4a7b-a437-e81a2ad9204f","resolution":{"observed_at":"2026-08-12T19:07:19.550338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.523473Z","title":"Provably consistent partial- label learning,","venue":null,"work_id":"35aadb0c-581a-4171-ae2c-1e6ecda165d5","year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.982631Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:6b6e07734cb84a6688e360673f69529b5a8bf40c5d481ccc721e88f0e7b6f2d8","observation_id":"98506885-f0fb-492d-982f-15b2e8dfd2af","resolution":{"observed_at":"2026-08-12T19:07:19.528621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.509366Z","title":"Partial label learning with batch label correction,","venue":null,"work_id":"72ed99a3-8346-4497-ba62-220bd76d8a69","year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.987199Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:924d8d2bae364a0c06769d2b57176125ecb1a30b4abb71089d017480f6f46176","observation_id":"33d5064b-c807-49f9-a7e4-7d797a19dac5","resolution":{"observed_at":"2026-08-12T19:07:19.514396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.494614Z","title":"Making deep neural networks robust to label noise: A loss correction approach,","venue":null,"work_id":"60603b5a-9281-4752-b7d4-ce1ac4619aa4","year":2017},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.992876Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:8ab0f49ef003723fa8e79f5f8121075090fb57e3fb92edae0b95c8930f6b6db7","observation_id":"ff26d6f1-0eef-485b-a9ed-fd19b9583fde","resolution":{"observed_at":"2026-08-12T19:07:19.499020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:18.997197Z","title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:18.997197Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:eb158587bb1f241a0ae0f82d558c753228bceb01f26f4318b71dcd54de7f038d","observation_id":"f11db8ab-bf2c-4592-b1a1-533526d78895","resolution":{"observed_at":"2026-08-12T19:07:18.997197Z","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":"2026-08-12T19:07:19.466817Z","title":"How does disagreement help generaliza- tion against label corruption?","venue":null,"work_id":"4db38955-889b-4027-ba9f-dc5d1ebfa4bf","year":2019},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.005369Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:5e0f2b809845ac4ad53d706698c0d29b3b27f9da2dead1cf174f7b5703107a0a","observation_id":"ad8ae49c-4415-4e31-a821-caa67aa6bbfa","resolution":{"observed_at":"2026-08-12T19:07:19.473080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.453845Z","title":"Generalized cross entropy loss for training deep neural networks with noisy la- bels,","venue":null,"work_id":"005cf64e-1d26-43b1-b924-e6657c49be1a","year":2018},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.011739Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:e8d3da2a66ac2187b79d2781c4843cab4537880a51412a6f86635c68b3b9529e","observation_id":"3302481a-66dc-4031-95bf-4567b3d6fcbf","resolution":{"observed_at":"2026-08-12T19:07:19.458246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02347","last_updated":"2021-03-22T22:01:05Z","snapshot_observed_at":"2026-08-17T18:15:32.848188Z","submitted_at":"2020-10-05T21:44:09Z","title":"Learning with Instance-Dependent Label Noise: A Sample Sieve Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02347","snapshot_observed_at":"2026-08-12T19:07:19.018105Z","title":"Learning with instance-dependent label noise: A sample sieve approach,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.018105Z"},"links":{"cited_paper":"/paper/2010.02347","citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:6bb5499d7b7035e3e51957d3a1a6e3e76235f6d0b86ee32a3a6d039ea787e369","observation_id":"2576ba2d-6c34-4e35-a725-19496d15133b","resolution":{"observed_at":"2026-08-12T19:07:19.018105Z","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":"2026-08-12T19:07:19.440009Z","title":"Class2simi: A noise reduction per- spective on learning with noisy labels,","venue":null,"work_id":"46fe45b6-f6e0-4532-ba84-da44fa37814b","year":2021},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.025567Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:b6fa5ca3f65304efeb1c452553ec09550ad86a55d254bd856892b1cfa5c188e9","observation_id":"5e3b73ba-6e97-4902-997a-85265e45c7a6","resolution":{"observed_at":"2026-08-12T19:07:19.444355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.428335Z","title":"Instance-dependent label- noise learning with manifold-regularized transition ma- trix estimation,","venue":null,"work_id":"18d697b9-6ab9-45e3-a9a7-13704c361325","year":2022},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.030952Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:9996bdc5d752a0bb12bb11ffc82a82443191885babf6db554d5693d8aca3b780","observation_id":"db540814-aa3f-4fea-80f8-823ff4a3f96a","resolution":{"observed_at":"2026-08-12T19:07:19.432286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.415691Z","title":"Peer loss functions: Learning from noisy labels without knowing noise rates,","venue":null,"work_id":"d279048b-4802-4c64-b53c-1c85b770272f","year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.035447Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:4eecc233e4ed255c0ae0a309287700c65d4ac786d2c42371df09bd91d1887555","observation_id":"145e7ba8-834c-4ace-8d6a-e8a61051dbbe","resolution":{"observed_at":"2026-08-12T19:07:19.419807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.039751Z","title":"Robust loss functions under label noise for deep neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.039751Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:3d6a095f857d7ff2c6f14b36459d7f8a9081bd3e86583070a51c3a857c55deab","observation_id":"f42566a6-435e-4343-b821-8931c0b6e657","resolution":{"observed_at":"2026-08-12T19:07:19.039751Z","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":"2026-08-12T19:07:19.396023Z","title":"Learning from noisy labels with complementary loss functions,","venue":null,"work_id":"73ecfc77-2f50-4ac9-a335-0fe67202b436","year":2021},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.044278Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:1b8e43de67ca725b8226772c3b7005856654aaa925f09da9c9f4f4104323f87f","observation_id":"5f6cce1e-fff0-4656-9117-266831455207","resolution":{"observed_at":"2026-08-12T19:07:19.400063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.382293Z","title":"Neural message passing for quantum chem- istry,","venue":null,"work_id":"d3558ab2-0181-4620-aaef-b54165677bba","year":2017},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.048636Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:6caac03a179b61c0193d1acc7130ee065ec59a9c65be15401e2a084ce940f4b3","observation_id":"5e2306ab-40f6-4bb7-8322-f0c4caacb245","resolution":{"observed_at":"2026-08-12T19:07:19.387246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.370511Z","title":"Dual t: Reducing estimation error for transition matrix in label-noise learning,","venue":null,"work_id":"7d3cf20d-cb34-4228-9ca2-35999b3ca29f","year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.053905Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:60654bc84b4bd78fac2a431fdbe1887237d43ec74bd7265ad8a0ec2cabb4d8bb","observation_id":"f2508c06-5ad6-466e-938f-77089b31d180","resolution":{"observed_at":"2026-08-12T19:07:19.374905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.358088Z","title":"Part-dependent label noise: Towards instance-dependent label noise,","venue":null,"work_id":"78870d65-fdac-44d0-81f6-7af6db1932d3","year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.058981Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:cd012454c237c4f877c7d40ff265fc31214cc9dc0c301d0ee927c0bcde648b20","observation_id":"778e18c4-29fc-486e-994a-94ea54c7c828","resolution":{"observed_at":"2026-08-12T19:07:19.362586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.344756Z","title":"Estimating instance-dependent bayes-label transition matrix using a deep neural network,","venue":null,"work_id":"8bab15e9-b205-4db4-84b0-04a221698ed3","year":2022},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.063356Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:0d0cff486aa02b21df15e0a3bcdf9fdad44197fbe32139be87dce3ae25bb6671","observation_id":"8e3db5c1-487a-416b-b488-9e5924c87110","resolution":{"observed_at":"2026-08-12T19:07:19.349231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.330061Z","title":"L dmi: A novel information-theoretic loss function for training deep nets robust to label noise,","venue":null,"work_id":"169e698c-50c1-47e0-9e7e-90897fe3bb71","year":2019},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.068023Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:dd17a3f4fc3cc5d9a754305e3d2aa6609fad82623efd325c1821aea01c26af6b","observation_id":"6144d291-954e-4227-b548-6fd4bd22b0b2","resolution":{"observed_at":"2026-08-12T19:07:19.334437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.315752Z","title":"Classification with noisy labels by importance reweighting,","venue":null,"work_id":"f4401900-8231-4905-8ffb-de01adeb4caf","year":2015},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.072126Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:b8ceefd4ec34e1a52966e3e163bf66a50b2b9b2bb9319efc97644bd9bc9d9679","observation_id":"d187329e-6a45-4404-86c6-df1f2352c0f9","resolution":{"observed_at":"2026-08-12T19:07:19.319915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.302255Z","title":"Birds of a feather: Homophily in social networks,","venue":null,"work_id":"04cbb255-da07-4820-aee7-9b80357d91fe","year":2001},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.076438Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:42e1ce18b7677efff7a8784b49fe96c4cfa6b747aecc57e95bf8eb0855048b8d","observation_id":"85d6c79e-4739-43d4-8cf5-98e5c69f31b0","resolution":{"observed_at":"2026-08-12T19:07:19.306404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.288910Z","title":"Hierarchical grammar-induced geometry for data-efficient molecular property predic- tion,","venue":null,"work_id":"8db53842-a5e2-43e1-a7cd-bcd72c03eea5","year":2023},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.081817Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:99777e671778569385a600707e2ca16fe83af073ea7296208317d35c7b0129d9","observation_id":"e8c59c9b-b112-4838-8b6e-8684d1628183","resolution":{"observed_at":"2026-08-12T19:07:19.293170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.085979Z","title":"Collective classification in network data,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.085979Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:3ff4054ff26ac298922bc260c0e253180345118d9926aec1649c01812842b3f2","observation_id":"d8ff7973-8842-4a7c-a849-536fa053bbd5","resolution":{"observed_at":"2026-08-12T19:07:19.085979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13522","last_updated":"2023-06-21T23:30:52Z","snapshot_observed_at":"2026-08-16T15:52:33.485863Z","submitted_at":"2023-02-27T05:21:35Z","title":"IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13522","snapshot_observed_at":"2026-08-12T19:07:19.089711Z","title":"Igb: Addressing the gaps in label- ing, features, heterogeneity, and size of public graph datasets for deep learning research,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.089711Z"},"links":{"cited_paper":"/paper/2302.13522","citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:e6ddf9b9d9e4328d10a65ad53b303721b194f5622495a9518acee0ddf71f5707","observation_id":"1ce1bb25-63b9-4e82-a5b2-18738c6a3846","resolution":{"observed_at":"2026-08-12T19:07:19.089711Z","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":"2026-08-12T19:07:19.094976Z","title":"Open graph benchmark: Datasets for machine learning on graphs,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.094976Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:7625b161e5d06c6f07be62747ccb0c69e69a52a74df83792344d5b57a526d1d1","observation_id":"e02da6e8-f18f-41a4-8d6a-aa00b2bd7cb5","resolution":{"observed_at":"2026-08-12T19:07:19.094976Z","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":"2026-08-12T19:07:19.258136Z","title":"Learning from massive noisy labeled data for image classification,","venue":null,"work_id":"b5172d1d-8df5-453c-8e79-23f0ef4fa29d","year":2015},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.099698Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:f67e6d8446a2712de514a7dc0206351f82dda088e57050b795c255f1bd3c6825","observation_id":"8c28302a-c3d6-452c-8fd2-1cc516407808","resolution":{"observed_at":"2026-08-12T19:07:19.262313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T19:07:19.242604Z","title":"Deeper insights into graph convolutional networks for semi-supervised learning,","venue":null,"work_id":"8842b6de-9595-419a-b4e8-7ebd1124aa12","year":2018},"citing_paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T19:07:19.106455Z"},"links":{"citing_paper":"/paper/2411.11020"},"observation_digest":"sha256:58430154b8358aab6866fb91a7cd059dd58fc0be3ff5c9c4b1f9cfe2286491d1","observation_id":"d1ced94a-69e3-4ae1-b58f-0cc26f0229dd","resolution":{"observed_at":"2026-08-12T19:07:19.248886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.11020","last_updated":"2024-11-17T09:52:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T05:09:18.955895Z","submitted_at":"2024-11-17T09:52:20Z","title":"Training a Label-Noise-Resistant GNN with Reduced Complexity"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":37},"total_outbound_references":48},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2411.11020."}