{"as_of":"2026-08-08T18:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:547377feb716c30a0af56aab9f13ac58c7b598f48b2d37188c90f9fbfa76f4b9","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":"2026-08-07T00:55:54.842692Z","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":"2026-08-07T00:55:54.941360Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.04299","last_updated":"2024-06-07T03:09:35Z","snapshot_observed_at":"2026-08-05T12:37:12.699768Z","submitted_at":"2024-06-06T17:45:00Z","title":"NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise","version":2},"cited_work":{"arxiv_id":"2406.04299","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.04299","snapshot_observed_at":"2026-08-07T00:55:54.941360Z","title":"NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise","venue":"cs.LG","work_id":"1d080eef-3578-411d-93c0-ae3dfee89539","year":2024},"citing_paper":{"arxiv_id":"2506.12468","last_updated":"2025-06-17T03:17:11Z","snapshot_observed_at":"2026-08-07T00:46:40.868394Z","submitted_at":"2025-06-14T12:14:15Z","title":"Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:54.842692Z"},"links":{"cited_paper":"/paper/2406.04299","citing_paper":"/paper/2506.12468"},"observation_digest":"sha256:671ddad16df5f86869ec9f14a9587732700efbe88bbd54b54cda574b0eb32873","observation_id":"c7ee6925-deff-4bb9-ac39-ed403865d7a5","resolution":{"observed_at":"2026-08-07T00:55:54.946991Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04299","last_updated":"2024-06-07T03:09:35Z","snapshot_observed_at":"2026-08-05T12:37:12.699768Z","submitted_at":"2024-06-06T17:45:00Z","title":"NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04299","snapshot_observed_at":"2026-08-01T13:29:53.613715Z","title":"Advances in Neural Information Processing Systems Datasets and Benchmarks Track , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19108","last_updated":"2026-07-21T13:50:24Z","snapshot_observed_at":"2026-08-08T00:54:55.183688Z","submitted_at":"2026-07-21T13:50:24Z","title":"OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T13:29:53.613715Z"},"links":{"cited_paper":"/paper/2406.04299","citing_paper":"/paper/2607.19108"},"observation_digest":"sha256:c1105515bfe5d97675f46eb814d8a965b96da74e8770a1b97b11ec355314843a","observation_id":"527bdd1a-ecd6-4d47-9d48-5422dfda2106","resolution":{"observed_at":"2026-08-01T13:29:53.613715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2406.04299/citation-record","integrity":"/paper/2406.04299/integrity","json":"/paper/2406.04299/citation-record.json","paper":"/paper/2406.04299"},"outbound":[],"paper":{"arxiv_id":"2406.04299","last_updated":"2024-06-07T03:09:35Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T12:37:12.699768Z","submitted_at":"2024-06-06T17:45:00Z","title":"NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise"},"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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.04299."}