{"as_of":"2026-08-19T12:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:05409ec6a0ca4476d0aecbe71d01cc83481e320e104f1f532e6a4253b801ce4c","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":"2026-08-19T06:32:44.657259+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":"2026-08-10T22:08:18.085536Z","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":"2026-08-08T14:26:02.997087Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.08401","last_updated":"2024-02-13T12:02:37Z","snapshot_observed_at":"2026-08-16T14:19:00.302962Z","submitted_at":"2024-02-13T12:02:37Z","title":"LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.08401","snapshot_observed_at":"2026-08-10T22:08:18.085536Z","title":"Loss-gat: Label propa- gation and one-class semi-supervised graph attention network for fake news detection.arXiv preprint arXiv:2402.08401, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.03290","last_updated":"2025-01-06T07:18:31Z","snapshot_observed_at":"2026-08-18T02:54:28.793640Z","submitted_at":"2025-01-06T07:18:31Z","title":"A Decision-Based Heterogenous Graph Attention Network for Multi-Class Fake News Detection","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T22:08:18.085536Z"},"links":{"cited_paper":"/paper/2402.08401","citing_paper":"/paper/2501.03290"},"observation_digest":"sha256:0a03c1b8d7b3c31cdc49b4007b4738128ba81fc6f8dd4d960c450e2489c6943a","observation_id":"e017d7e7-8457-48dc-8b91-d02a149e453c","resolution":{"observed_at":"2026-08-10T22:08:18.085536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.08401","last_updated":"2024-02-13T12:02:37Z","snapshot_observed_at":"2026-08-16T14:19:00.302962Z","submitted_at":"2024-02-13T12:02:37Z","title":"LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection","version":1},"cited_work":{"arxiv_id":"2402.08401","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.08401","snapshot_observed_at":"2026-08-08T14:26:02.997087Z","title":"LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection","venue":"cs.LG","work_id":"ac101964-49c8-41de-b73b-8487f14f458f","year":2024},"citing_paper":{"arxiv_id":"2502.06927","last_updated":"2025-02-10T18:51:57Z","snapshot_observed_at":"2026-08-12T02:12:28.698446Z","submitted_at":"2025-02-10T18:51:57Z","title":"Neighborhood-Order Learning Graph Attention Network for Fake News Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:02.534855Z"},"links":{"cited_paper":"/paper/2402.08401","citing_paper":"/paper/2502.06927"},"observation_digest":"sha256:03f379e7bc3076432f9d6a0891bdaa038bead08eea5bf685f80f16ecc3a18aea","observation_id":"3490efea-6650-43a6-b747-b9eac0ff46cd","resolution":{"observed_at":"2026-08-08T14:26:03.001859Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.08401/citation-record","integrity":"/paper/2402.08401/integrity","json":"/paper/2402.08401/citation-record.json","paper":"/paper/2402.08401"},"outbound":[],"paper":{"arxiv_id":"2402.08401","last_updated":"2024-02-13T12:02:37Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T14:19:00.302962Z","submitted_at":"2024-02-13T12:02:37Z","title":"LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.08401."}