{"as_of":"2026-08-15T15:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cd6e03128e60456d519958471b1b67ac7075401d58306f3e2ef02e8cf6906eb9","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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":"2026-08-11T10:51:16.243995Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-11T10:51:16.428059Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.00216","last_updated":"2023-08-25T07:26:35Z","snapshot_observed_at":"2026-08-15T00:26:30.995640Z","submitted_at":"2022-11-01T01:57:00Z","title":"Distributed Graph Neural Network Training: A Survey","version":2},"cited_work":{"arxiv_id":"2211.00216","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.00216","snapshot_observed_at":"2026-08-11T10:51:16.428059Z","title":"Distributed Graph Neural Network Training: A Survey","venue":"cs.LG","work_id":"56e0ff90-7e24-4ee3-9413-586a5b47a1cf","year":2022},"citing_paper":{"arxiv_id":"2412.16144","last_updated":"2024-12-20T18:48:46Z","snapshot_observed_at":"2026-08-14T02:27:42.092097Z","submitted_at":"2024-12-20T18:48:46Z","title":"FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T10:51:16.243995Z"},"links":{"cited_paper":"/paper/2211.00216","citing_paper":"/paper/2412.16144"},"observation_digest":"sha256:6d563b2a13d42af770c056f8b380a0a0c31fbb85600a93b0e3cc09f77058c0de","observation_id":"10d774cb-bfad-4ef2-9f21-cb3db37b640c","resolution":{"observed_at":"2026-08-11T10:51:16.431941Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2211.00216/citation-record","integrity":"/paper/2211.00216/integrity","json":"/paper/2211.00216/citation-record.json","paper":"/paper/2211.00216"},"outbound":[],"paper":{"arxiv_id":"2211.00216","last_updated":"2023-08-25T07:26:35Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T00:26:30.995640Z","submitted_at":"2022-11-01T01:57:00Z","title":"Distributed Graph Neural Network Training: A Survey"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2211.00216."}