{"as_of":"2026-08-08T02:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:286551e2f61f1d5d1e1c90fc9ae86609dcf73a8f543175bed65bed9b2bc80c2e","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:12:04.524177Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T17:05:50.461992Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.14883","last_updated":"2022-06-30T18:35:43Z","snapshot_observed_at":"2026-07-06T12:53:43.089970Z","submitted_at":"2022-03-28T16:41:18Z","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs","version":2},"cited_work":{"arxiv_id":"2203.14883","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.14883","snapshot_observed_at":"2026-07-01T17:05:50.461992Z","title":"arXiv preprint arXiv:2203.14883 , author =","venue":null,"work_id":"f96577fb-ee6f-4768-814c-40d212cffc05","year":2022},"citing_paper":{"arxiv_id":"2411.11259","last_updated":"2026-04-13T03:59:44Z","snapshot_observed_at":"2026-07-06T19:51:41.646058Z","submitted_at":"2024-11-18T03:28:11Z","title":"Graph Retention Networks for Dynamic Graphs","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-23T17:44:20.434591Z"},"links":{"cited_paper":"/paper/2203.14883","citing_paper":"/paper/2411.11259"},"observation_digest":"sha256:d1a386d92bdd1d24bcde0d63640b1d33e4ba432d566875a48f8e5506e1e7805f","observation_id":"9fa6d828-d76f-4017-83a9-13e8b8bc1660","resolution":{"observed_at":"2026-05-23T17:45:46.134172Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.14883","last_updated":"2022-06-30T18:35:43Z","snapshot_observed_at":"2026-07-06T12:53:43.089970Z","submitted_at":"2022-03-28T16:41:18Z","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.14883","snapshot_observed_at":"2026-08-06T23:12:04.524177Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.19282","last_updated":"2025-06-24T03:31:43Z","snapshot_observed_at":"2026-08-07T02:46:32.003410Z","submitted_at":"2025-06-24T03:31:43Z","title":"A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:04.524177Z"},"links":{"cited_paper":"/paper/2203.14883","citing_paper":"/paper/2506.19282"},"observation_digest":"sha256:72bb7afce1707f09bfcb8af61fac5d17b19dcb155aa7f582a0ab0a4fccbc83b5","observation_id":"6823225b-f6b5-4cd2-b4ef-fe36ab1ea90f","resolution":{"observed_at":"2026-08-06T23:12:04.524177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.14883","last_updated":"2022-06-30T18:35:43Z","snapshot_observed_at":"2026-07-06T12:53:43.089970Z","submitted_at":"2022-03-28T16:41:18Z","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.14883","snapshot_observed_at":"2026-08-06T20:44:44.195182Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02151","last_updated":"2025-07-02T21:15:00Z","snapshot_observed_at":"2026-08-07T15:14:43.028614Z","submitted_at":"2025-07-02T21:15:00Z","title":"Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T20:44:44.195182Z"},"links":{"cited_paper":"/paper/2203.14883","citing_paper":"/paper/2507.02151"},"observation_digest":"sha256:d542519985ec59387bc8198666fa45fff46ab4d34eba5eec7576649c5c2c1f37","observation_id":"82aa4507-61ba-443a-8f91-88b937940cc6","resolution":{"observed_at":"2026-08-06T20:44:44.195182Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.14883","last_updated":"2022-06-30T18:35:43Z","snapshot_observed_at":"2026-07-06T12:53:43.089970Z","submitted_at":"2022-03-28T16:41:18Z","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.14883","snapshot_observed_at":"2026-08-06T16:23:04.865492Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13825","last_updated":"2025-07-18T11:29:15Z","snapshot_observed_at":"2026-08-06T16:13:17.543071Z","submitted_at":"2025-07-18T11:29:15Z","title":"When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:04.865492Z"},"links":{"cited_paper":"/paper/2203.14883","citing_paper":"/paper/2507.13825"},"observation_digest":"sha256:fe92eacc5a063c8b2789bf6c3ba09f91b6fc901919c43062d78c3b44b038bce6","observation_id":"370bb151-6a38-44fc-b083-edd852beffe4","resolution":{"observed_at":"2026-08-06T16:23:04.865492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.14883","last_updated":"2022-06-30T18:35:43Z","snapshot_observed_at":"2026-07-06T12:53:43.089970Z","submitted_at":"2022-03-28T16:41:18Z","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs","version":2},"cited_work":{"arxiv_id":"2203.14883","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.14883","snapshot_observed_at":"2026-07-01T17:05:50.461992Z","title":"arXiv preprint arXiv:2203.14883 , author =","venue":null,"work_id":"f96577fb-ee6f-4768-814c-40d212cffc05","year":2022},"citing_paper":{"arxiv_id":"2606.28225","last_updated":"2026-07-08T23:17:43Z","snapshot_observed_at":"2026-07-15T23:17:48.886219Z","submitted_at":"2026-06-26T16:13:48Z","title":"Estimation-Prediction Tradeoff in Causal Probabilistic Temporal Graphs","version":1},"reference_index":242,"source":"arxiv_source","source_observed_at":"2026-06-29T04:20:15.198382Z"},"links":{"cited_paper":"/paper/2203.14883","citing_paper":"/paper/2606.28225"},"observation_digest":"sha256:2ae52cdd018a2d9b03c8acfbd1b82d2eb3ac213b56cf0ea81de141433a0ed74c","observation_id":"3a1da72b-af23-47e4-9044-c2ac995b18de","resolution":{"observed_at":"2026-07-01T17:05:50.463422Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.14883","last_updated":"2022-06-30T18:35:43Z","snapshot_observed_at":"2026-07-06T12:53:43.089970Z","submitted_at":"2022-03-28T16:41:18Z","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.14883","snapshot_observed_at":"2026-07-12T06:29:01.304756Z","title":"Tgl: A general frame- work for temporal gnn training on billion-scale graphs","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.02872","last_updated":"2026-07-03T02:19:52Z","snapshot_observed_at":"2026-08-06T06:24:35.834240Z","submitted_at":"2026-07-03T02:19:52Z","title":"Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-12T06:29:01.304756Z"},"links":{"cited_paper":"/paper/2203.14883","citing_paper":"/paper/2607.02872"},"observation_digest":"sha256:75f0da22bc87c40fb710e1c9546edcf9d4dddac1c5caf104e341c2376bf58e2b","observation_id":"36529ec9-8e94-4f61-b493-d4e8c646d20c","resolution":{"observed_at":"2026-07-12T06:29:01.304756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2203.14883/citation-record","integrity":"/paper/2203.14883/integrity","json":"/paper/2203.14883/citation-record.json","paper":"/paper/2203.14883"},"outbound":[],"paper":{"arxiv_id":"2203.14883","last_updated":"2022-06-30T18:35:43Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T12:53:43.089970Z","submitted_at":"2022-03-28T16:41:18Z","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2203.14883."}