{"as_of":"2026-08-09T02:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:00f1f834e53ce9f8a1aaf3e581b7b849f6fc62b2cbcafe62bfc1d7d1cfaad53e","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:32:06.667371Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T02:57:52.488144Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07854","snapshot_observed_at":"2026-08-01T02:57:52.488144Z","title":"Residual reweighted conformal prediction for graph neural networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25273","last_updated":"2026-07-28T04:20:34Z","snapshot_observed_at":"2026-08-05T10:36:26.620712Z","submitted_at":"2026-07-28T04:20:34Z","title":"HeAD-CP: Heterophily-Aware Diffused Conformal Prediction Sets for Graph Neural Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T02:57:52.488144Z"},"links":{"cited_paper":"/paper/2506.07854","citing_paper":"/paper/2607.25273"},"observation_digest":"sha256:e798b876d51edaab39ef7eff82af0b9b3f9306b8a0dc7b6c32bc4b1515d1266c","observation_id":"17be7e6e-de27-4d0f-b15e-e16980c91e18","resolution":{"observed_at":"2026-08-01T02:57:52.488144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.07854/citation-record","integrity":"/paper/2506.07854/integrity","json":"/paper/2506.07854/citation-record.json","paper":"/paper/2506.07854"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:32:08.098357Z","title":null,"venue":null,"work_id":"b28ff4a7-d01a-48d4-bf20-38c257662e0a","year":2020},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.311293Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:56bfba9fc2d7bb102d7c08eff4fb47efe23f176ca3c03e996a8263857b7cd9cc","observation_id":"139345fd-d018-4ead-a6fc-8e75b3e736ab","resolution":{"observed_at":"2026-08-07T05:32:08.142304Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.10181","last_updated":"2020-07-10T18:22:12Z","snapshot_observed_at":"2026-07-06T09:14:10.074582Z","submitted_at":"2020-04-21T17:45:38Z","title":"Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.10181","snapshot_observed_at":"2026-08-07T05:32:04.952323Z","title":"Know- ing what you know: valid and validated confidence sets in multiclass and multilabel prediction","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:04.952323Z"},"links":{"cited_paper":"/paper/2004.10181","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:5b8df92cb0c493212cac3cac014b360f1954355211764bff1d38fddbf79f8455","observation_id":"f5d39b90-3f13-4df9-b179-7bb43730afd5","resolution":{"observed_at":"2026-08-07T05:32:04.952323Z","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":"2026-07-06T05:10:16.862707Z","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":"2026-08-07T05:32:05.204889Z","title":"Semi-supervised classifi- cation with graph convolutional networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.204889Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:b5cff9ee3e2459d07515415c741018fe9ba4f5180c1139958ed14003a9e8f8f2","observation_id":"2ca74de7-c74f-4351-8b35-c8bb67bd2005","resolution":{"observed_at":"2026-08-07T05:32:05.204889Z","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":"2026-08-07T05:32:09.196244Z","title":"Segregation in social networks: Markov bridge models and estimation","venue":null,"work_id":"7249a53d-dbac-43e0-bab9-6af12312ea67","year":2021},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.265795Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:b7d29a82778762adab40255540e5383095f7a8a68c3e3baf1ebb6dc600679786","observation_id":"9a173f7b-4997-40d7-aafd-a2e94ecf32d4","resolution":{"observed_at":"2026-08-07T05:32:09.284179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.07611","last_updated":"2025-04-10T10:01:06Z","snapshot_observed_at":"2026-08-07T16:06:54.644825Z","submitted_at":"2025-04-10T10:01:06Z","title":"Conditional Conformal Risk Adaptation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.07611","snapshot_observed_at":"2026-08-07T05:32:05.357191Z","title":"Conformity score averaging for classification","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.357191Z"},"links":{"cited_paper":"/paper/2504.07611","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:ec17822b77df9c62082ebb5405243a0fd43e00a36d9e8a229f9d8e11dddcad6b","observation_id":"4253d542-8115-4272-beda-9d4ac3df7f8e","resolution":{"observed_at":"2026-08-07T05:32:05.357191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.07076","last_updated":"2023-05-01T20:18:08Z","snapshot_observed_at":"2026-08-08T11:14:38.931898Z","submitted_at":"2022-01-18T16:00:03Z","title":"Mitigating Misinformation Spread on Blockchain Enabled Social Media Networks","version":3},"cited_work":{"arxiv_id":"2201.07076","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.07076","snapshot_observed_at":"2026-08-07T05:32:06.845947Z","title":"Mitigating Misinformation Spread on Blockchain Enabled Social Media Networks","venue":"cs.SI","work_id":"00794a45-2a5a-4e3e-8ec1-53d001a52e34","year":2022},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.468786Z"},"links":{"cited_paper":"/paper/2201.07076","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:8dc02d023bfeb613ffe196ecec1129b68f4ffca5b25aff0d4afffafefa8273eb","observation_id":"9a901d7e-4ef0-4957-8ac4-f515de5c133c","resolution":{"observed_at":"2026-08-07T05:32:06.930691Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:07.509036Z","title":"NE means noise edge","venue":null,"work_id":"81792ebd-3d25-40e6-ac82-8fb5dd952715","year":2024},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.608297Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:3a8bc9c3903788e56eca98a29113205820a3b445c82aa043f28a3ef3ca1e88fc","observation_id":"5b21207c-33a2-400f-b8e2-8b571de379a5","resolution":{"observed_at":"2026-08-07T05:32:07.631676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-07T05:32:06.045884Z","title":"Graph attention networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.045884Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:9fecb911635b2e7d0ccbe2432f184371e06d2d848c09638fbfba3e7b881487a3","observation_id":"573b323a-4030-4c36-acdb-ba3169678585","resolution":{"observed_at":"2026-08-07T05:32:06.045884Z","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":"2026-08-07T05:32:08.622053Z","title":"Nopeek: Information leakage reduc- tion to share activations in distributed deep learning","venue":null,"work_id":"ae7bc580-db20-4e52-a7b0-18340635496e","year":2020},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.095937Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:b3b96d002a9282f289fc0ab6de960078d85613b58e5f4e18a1bd6f930ef0c5fb","observation_id":"bbb4678b-b7f0-4eac-a804-e6032e927794","resolution":{"observed_at":"2026-08-07T05:32:08.736782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:08.397639Z","title":"Heterogeneous graph attention network","venue":null,"work_id":"bfbd747a-c1c8-4c1d-9028-f784bf5472ba","year":2022},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.147006Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:79963c107251e1cc7a8a4edab2851bb24aeb04d972b6af72c9903011ff4b9fa9","observation_id":"9d4f4f0f-50f1-467c-91c9-087758c6475c","resolution":{"observed_at":"2026-08-07T05:32:08.496788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:08.241842Z","title":"Assume the graph has n nodes with f features","venue":null,"work_id":"e4216fb8-ffe3-4eac-b2d3-6c454cf44834","year":2022},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.234742Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:afada552b881b69618f6c855d0e354e2320867d85965eabd7d84f9cf9ef69533","observation_id":"a6d6ccfa-9c6a-4bf2-b450-e97b3fb83b3c","resolution":{"observed_at":"2026-08-07T05:32:08.292372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:07.998721Z","title":"6.3 APPENDICES FIGURES 6.3.1 Schematic figure for transductive and inductive settings for link prediction","venue":null,"work_id":"76a55401-dd85-4889-9bf0-4d8fbfdfeb57","year":2016},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.361640Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:97a0cec3e6ea8bc69744f0d8ebd70fbe77832e8f5aa95340b2dd892587af236d","observation_id":"77ae7df4-5e07-4553-8f0e-929e8e8e6dde","resolution":{"observed_at":"2026-08-07T05:32:08.045263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:07.901141Z","title":null,"venue":null,"work_id":"a13c3360-6eac-41fa-b6a6-1c3bb180b7c8","year":2020},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.420450Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:e9c4b5a541a087d88ab92a34d4e271046b639288d967f76b8f730d6ff809ef90","observation_id":"b33922d9-33bc-47b5-8eeb-9f93d2c60add","resolution":{"observed_at":"2026-08-07T05:32:07.942957Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:07.798313Z","title":"The probability is over the datagenerating distribution","venue":null,"work_id":"0cc6cba9-8ad4-4787-9366-cdd191abf6ae","year":2002},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.480323Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:c86701bfe771fb0ada16254e457b2351a7ae6e878f88d6b8280e4c7642993445","observation_id":"9cf350cf-0f00-43ef-a79b-32bcc850f154","resolution":{"observed_at":"2026-08-07T05:32:07.828372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:07.316658Z","title":null,"venue":null,"work_id":"1744db04-2028-4fb5-84ce-348e6c613c01","year":null},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.667371Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:6fc6630492122a0c4de1f70f795a19f064517f64ac5ebb9a731570afc1e4ffc9","observation_id":"2f8cae9d-874c-4a04-aa56-91b8589e0047","resolution":{"observed_at":"2026-08-07T05:32:07.392398Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:08.928178Z","title":"Normalized nonconformity measures for regression con- formal prediction","venue":null,"work_id":"67413510-ceb5-4fe0-a533-7cf06e920e1c","year":2008},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2002,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.866603Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:827f763a0b45daaa3aebbdc40ec6c099a1509446e976318542378fb3293da149","observation_id":"7a64095e-5399-4686-8a31-b6d96a2f882b","resolution":{"observed_at":"2026-08-07T05:32:09.004721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.01558","last_updated":"2024-11-03T13:15:32Z","snapshot_observed_at":"2026-07-06T19:44:16.540551Z","submitted_at":"2024-11-03T13:15:32Z","title":"Adaptive Conformal Inference by Particle Filtering under Hidden Markov Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.01558","snapshot_observed_at":"2026-08-07T05:32:05.965613Z","title":"URL https://doi.org/10.3150/10-BEJ267","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.965613Z"},"links":{"cited_paper":"/paper/2411.01558","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:6c8d63cb6017ebcbfc7ced7e38be3369384ed5f64feebc83ea0975bdc1f9abd8","observation_id":"f4f5d9b2-8ac5-4866-ad9b-d72424441bf4","resolution":{"observed_at":"2026-08-07T05:32:05.965613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.13092","last_updated":"2023-02-20T18:42:15Z","snapshot_observed_at":"2026-08-04T11:34:53.645635Z","submitted_at":"2022-06-27T07:53:38Z","title":"Split Localized Conformal Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.13092","snapshot_observed_at":"2026-08-07T05:32:05.022846Z","title":"Split localized conformal prediction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.022846Z"},"links":{"cited_paper":"/paper/2206.13092","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:3592a4af951a2529e1b64892f2744dcf7e5d33b275bc65e2868f64649b4d9ed4","observation_id":"b1ade2e2-a121-4e8b-a333-ff94e258fcfb","resolution":{"observed_at":"2026-08-07T05:32:05.022846Z","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":"2026-08-07T05:32:07.699513Z","title":null,"venue":null,"work_id":"f30a0147-79d5-402e-ab47-29d4557495ea","year":2019},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:06.552296Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:2b27bbdb14927346092892934c3f7acefbb3177afd949ba7db3ff0d7f07dde7b","observation_id":"47580f16-2a83-4aa3-996d-9203b9f8b52a","resolution":{"observed_at":"2026-08-07T05:32:07.745424Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:09.071318Z","title":"Inductive confidence machines for regression","venue":null,"work_id":"40a212f6-a350-4e53-96cf-495904e5753d","year":2002},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.802361Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:29e0a9d61ffed671683bff1e0bcbbfefdf26f81fa85ac9be9a5410d9fd22fea1","observation_id":"aa032897-9676-4d5f-9d6e-310d1d58ea3f","resolution":{"observed_at":"2026-08-07T05:32:09.132969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:32:08.804067Z","title":"Transportation networks for research core team","venue":null,"work_id":"80247455-de12-43ef-84ab-ebd0369042e9","year":2021},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.938157Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:c5f97ffbf143a0f36470de79c17b5bd9f3a98f075a83e8e63752bdbfff686cb1","observation_id":"6d579f7c-3489-4952-8c30-4208124e4509","resolution":{"observed_at":"2026-08-07T05:32:08.852512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.14193","last_updated":"2022-09-03T05:45:19Z","snapshot_observed_at":"2026-07-06T09:59:53.503386Z","submitted_at":"2020-09-29T17:58:04Z","title":"Uncertainty Sets for Image Classifiers using Conformal Prediction","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.14193","snapshot_observed_at":"2026-08-07T05:32:04.914311Z","title":"Uncertainty sets for image classifiers using conformal prediction","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:04.914311Z"},"links":{"cited_paper":"/paper/2009.14193","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:385d28f99b60e07bf2f3d2d7d11d75a67e115814dcef05bdd23919744a138ea5","observation_id":"d6a84511-a98f-419f-8831-6de1a5b706c3","resolution":{"observed_at":"2026-08-07T05:32:04.914311Z","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":"2026-08-07T05:32:09.552052Z","title":"Deep models under the gan: information leakage from collaborative deep learning","venue":null,"work_id":"e0a1656b-f1f1-4ef7-aaa1-bbde65706918","year":2017},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.091964Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:cf427c646a48c97fabd818aca587d31ea0cfef93480fada8f14adf3a70488813","observation_id":"1bd8e853-4d6a-4857-91a4-6391ed20db74","resolution":{"observed_at":"2026-08-07T05:32:09.626169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.04376","last_updated":"2025-03-04T04:56:21Z","snapshot_observed_at":"2026-07-06T19:46:28.260057Z","submitted_at":"2024-11-07T02:20:04Z","title":"Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.04376","snapshot_observed_at":"2026-08-07T05:32:05.639191Z","title":"Game- theoretic defenses for robust conformal prediction against adversarial attacks in medical imaging","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.639191Z"},"links":{"cited_paper":"/paper/2411.04376","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:44cf6fe73b9815aa2a9e49267bb8d272a13cd2696f1efc6463f3c13f8f573579","observation_id":"c3ebab58-5ce0-428c-b057-544fc1e65649","resolution":{"observed_at":"2026-08-07T05:32:05.639191Z","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":"2026-08-07T05:32:09.357749Z","title":"Bi-level atten- tion graph neural networks","venue":null,"work_id":"406a86fd-f01b-459b-9794-b6e4eccfa29d","year":2021},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.135787Z"},"links":{"citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:249fead243f69a067b04d359fb00702aa946b0d91e6e8eb43e89ea79bacedd21","observation_id":"77e3cd19-aa15-4a9e-a47e-db64ca0278e3","resolution":{"observed_at":"2026-08-07T05:32:09.461276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.16741","last_updated":"2023-03-29T14:48:51Z","snapshot_observed_at":"2026-07-06T15:09:32.824067Z","submitted_at":"2023-03-29T14:48:51Z","title":"Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution","version":1},"cited_work":{"arxiv_id":"2303.16741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.16741","snapshot_observed_at":"2026-08-07T05:32:07.055150Z","title":"Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution","venue":"cs.LG","work_id":"278f67e4-82b2-4c7b-a413-083e9b7c7e1f","year":2023},"citing_paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:05.291754Z"},"links":{"cited_paper":"/paper/2303.16741","citing_paper":"/paper/2506.07854"},"observation_digest":"sha256:074e671242712284e759fdd5e9ccf8a79d96df527229e2eddb67e67e97905ed5","observation_id":"a3f0c9eb-3005-4ad7-bb21-24010211cefd","resolution":{"observed_at":"2026-08-07T05:32:07.173070Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.07854","last_updated":"2025-06-09T15:19:17Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T05:21:40.204103Z","submitted_at":"2025-06-09T15:19:17Z","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":12,"verified_exact":1,"verified_fuzzy":12},"total_outbound_references":26},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2506.07854."}