{"as_of":"2026-08-09T02:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:01112e6c33a241c49f3baecb8b68c53058ac6ef11c3dccd532a148a8c322f930","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:06:29.215401Z","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":"2026-08-05T11:40:52.774585Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.06483","last_updated":"2022-04-15T15:12:58Z","snapshot_observed_at":"2026-08-07T14:06:53.006652Z","submitted_at":"2021-11-11T22:27:59Z","title":"Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.06483","snapshot_observed_at":"2026-08-06T20:06:29.215401Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.03840","last_updated":"2025-07-04T23:53:47Z","snapshot_observed_at":"2026-08-07T06:27:56.670360Z","submitted_at":"2025-07-04T23:53:47Z","title":"Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T20:06:29.215401Z"},"links":{"cited_paper":"/paper/2111.06483","citing_paper":"/paper/2507.03840"},"observation_digest":"sha256:4067b293f2daf899c2455279183cdf4346adf9a27242e00015a70fc71ae188f7","observation_id":"26d65709-a31f-47a6-8b43-dd79dc6b3f59","resolution":{"observed_at":"2026-08-06T20:06:29.215401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.06483","last_updated":"2022-04-15T15:12:58Z","snapshot_observed_at":"2026-08-07T14:06:53.006652Z","submitted_at":"2021-11-11T22:27:59Z","title":"Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs","version":3},"cited_work":{"arxiv_id":"2111.06483","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.06483","snapshot_observed_at":"2026-08-05T11:40:52.774585Z","title":"Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs","venue":"cs.LG","work_id":"4c98ddad-2762-4a62-9475-d74172d18a96","year":2021},"citing_paper":{"arxiv_id":"2509.02481","last_updated":"2025-09-02T16:31:40Z","snapshot_observed_at":"2026-08-08T09:06:46.281582Z","submitted_at":"2025-09-02T16:31:40Z","title":"HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T11:40:52.389985Z"},"links":{"cited_paper":"/paper/2111.06483","citing_paper":"/paper/2509.02481"},"observation_digest":"sha256:25fdbccb74268961d817f62868ad64a37d94f03d65457a7ebca267a928487c92","observation_id":"63722a54-acee-40f0-8f47-272f961a285a","resolution":{"observed_at":"2026-08-05T11:40:52.778875Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2111.06483/citation-record","integrity":"/paper/2111.06483/integrity","json":"/paper/2111.06483/citation-record.json","paper":"/paper/2111.06483"},"outbound":[],"paper":{"arxiv_id":"2111.06483","last_updated":"2022-04-15T15:12:58Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T14:06:53.006652Z","submitted_at":"2021-11-11T22:27:59Z","title":"Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large 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":"2026-08-08T06:32:00.761636+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 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.06483."}