{"as_of":"2026-08-19T07:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:369231fae26e11fa64d49238703100d8f47782573bc69e047a8612bd92372a42","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:38:09.388802Z","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":"2026-05-09T06:40:40.474257Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1909.03184","last_updated":"2019-09-10T01:14:33Z","snapshot_observed_at":"2026-08-13T18:11:43.604377Z","submitted_at":"2019-09-07T04:10:41Z","title":"Auto-GNN: Neural Architecture Search of Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.03184","snapshot_observed_at":"2026-08-14T15:38:09.388802Z","title":null,"venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"1908.00709","last_updated":"2021-04-16T03:38:23Z","snapshot_observed_at":"2026-08-16T04:58:09.012071Z","submitted_at":"2019-08-02T05:56:13Z","title":"AutoML: A Survey of the State-of-the-Art","version":6},"reference_index":278,"source":"pdf_text","source_observed_at":"2026-08-14T15:38:09.388802Z"},"links":{"cited_paper":"/paper/1909.03184","citing_paper":"/paper/1908.00709"},"observation_digest":"sha256:86d3e6133264482886ec898783a8b90a469b0259a804f3b5f821fc2730ff94b5","observation_id":"56ade6b2-eef0-46ed-a6b0-190e6c916e28","resolution":{"observed_at":"2026-08-14T15:38:09.388802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.03184","last_updated":"2019-09-10T01:14:33Z","snapshot_observed_at":"2026-08-13T18:11:43.604377Z","submitted_at":"2019-09-07T04:10:41Z","title":"Auto-GNN: Neural Architecture Search of Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.03184","snapshot_observed_at":"2026-08-11T23:48:48.494157Z","title":"Auto-gnn: Neural architecture search of graph neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02196","last_updated":"2024-12-03T06:21:35Z","snapshot_observed_at":"2026-08-18T09:02:53.192345Z","submitted_at":"2024-12-03T06:21:35Z","title":"SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:48:48.494157Z"},"links":{"cited_paper":"/paper/1909.03184","citing_paper":"/paper/2412.02196"},"observation_digest":"sha256:1f629d81b9fac051689a2a3aee64507701916c85694f995dec02a6cfa4ced05e","observation_id":"cdd4420e-bb4f-474d-9a89-4b7a50e67fa6","resolution":{"observed_at":"2026-08-11T23:48:48.494157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.03184","last_updated":"2019-09-10T01:14:33Z","snapshot_observed_at":"2026-08-13T18:11:43.604377Z","submitted_at":"2019-09-07T04:10:41Z","title":"Auto-GNN: Neural Architecture Search of Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.03184","snapshot_observed_at":"2026-08-10T21:10:54.410324Z","title":"Auto-gnn: Neural architecture search of graph neural networks,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.410324Z"},"links":{"cited_paper":"/paper/1909.03184","citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:1eae1e0c001fafc544f3b39cb9a45f83b43273e7c7d687bafb2615919ed960fa","observation_id":"b32b9f0a-2aaf-46dd-a9c6-303952fcd5c4","resolution":{"observed_at":"2026-08-10T21:10:54.410324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.03184","last_updated":"2019-09-10T01:14:33Z","snapshot_observed_at":"2026-08-13T18:11:43.604377Z","submitted_at":"2019-09-07T04:10:41Z","title":"Auto-GNN: Neural Architecture Search of Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"1909.03184","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.03184","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2d19af0f-da7c-43fa-9de5-5e3b652ccf7c","year":1909},"citing_paper":{"arxiv_id":"2605.05258","last_updated":"2026-05-06T04:37:02Z","snapshot_observed_at":"2026-07-31T06:44:35.140094Z","submitted_at":"2026-05-06T04:37:02Z","title":"PARNESS: A Paper Harness for End-to-End Automated Scientific Research with Dynamic Workflows, Full-Text Indexing, and Cross-Run Knowledge Accumulation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-08T18:13:49.834765Z"},"links":{"cited_paper":"/paper/1909.03184","citing_paper":"/paper/2605.05258"},"observation_digest":"sha256:4a6b98371049eb583425de6be3762a71ea8fd8a8ee18da141fbe7d04b25b3e51","observation_id":"d67dfa80-9947-4c54-877e-e4fbe64eb643","resolution":{"observed_at":"2026-05-09T06:40:40.476722Z","resolver_source":"arxiv_id","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/1909.03184/citation-record","integrity":"/paper/1909.03184/integrity","json":"/paper/1909.03184/citation-record.json","paper":"/paper/1909.03184"},"outbound":[],"paper":{"arxiv_id":"1909.03184","last_updated":"2019-09-10T01:14:33Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T18:11:43.604377Z","submitted_at":"2019-09-07T04:10:41Z","title":"Auto-GNN: Neural Architecture Search of Graph Neural Networks"},"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 4 inbound Pith citation observations for arXiv:1909.03184."}