{"as_of":"2026-08-20T09:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:06c43bf8a89937c16dd217253cd9874880f414025dc73ef79ee39b48bf0311d7","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:12:07.524163Z","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":"2026-08-10T22:53:27.192551Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2201.08549","last_updated":"2022-01-21T05:49:15Z","snapshot_observed_at":"2026-08-16T17:26:56.346369Z","submitted_at":"2022-01-21T05:49:15Z","title":"Fair Node Representation Learning via Adaptive Data Augmentation","version":1},"cited_work":{"arxiv_id":"2201.08549","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.08549","snapshot_observed_at":"2026-08-10T22:53:27.192551Z","title":"Fair Node Representation Learning via Adaptive Data Augmentation","venue":"cs.LG","work_id":"8b556df2-9433-4f5d-b3f5-4596bc89015c","year":2022},"citing_paper":{"arxiv_id":"2501.00595","last_updated":"2024-12-31T18:48:30Z","snapshot_observed_at":"2026-08-18T19:47:32.679188Z","submitted_at":"2024-12-31T18:48:30Z","title":"Unbiased GNN Learning via Fairness-Aware Subgraph Diffusion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T22:53:26.760312Z"},"links":{"cited_paper":"/paper/2201.08549","citing_paper":"/paper/2501.00595"},"observation_digest":"sha256:e74a00a94c1e0e5538fdde818a3c8524339de99b70febb4483638df2c877bd25","observation_id":"3fb5ec37-8fc0-4040-9e05-89dae682db82","resolution":{"observed_at":"2026-08-10T22:53:27.198845Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.08549","last_updated":"2022-01-21T05:49:15Z","snapshot_observed_at":"2026-08-16T17:26:56.346369Z","submitted_at":"2022-01-21T05:49:15Z","title":"Fair Node Representation Learning via Adaptive Data Augmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.08549","snapshot_observed_at":"2026-08-15T22:12:07.524163Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.07997","last_updated":"2025-05-19T02:28:16Z","snapshot_observed_at":"2026-08-18T22:24:11.462442Z","submitted_at":"2025-05-12T18:52:36Z","title":"FairZK: A Scalable System to Prove Machine Learning Fairness in Zero-Knowledge","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T22:12:07.524163Z"},"links":{"cited_paper":"/paper/2201.08549","citing_paper":"/paper/2505.07997"},"observation_digest":"sha256:23f2e26355c83fba4ad7e500896e16e9b9920e8886da20b14767e7281397fd4c","observation_id":"011910b3-baac-4276-909a-7be403a9dd11","resolution":{"observed_at":"2026-08-15T22:12:07.524163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.08549","last_updated":"2022-01-21T05:49:15Z","snapshot_observed_at":"2026-08-16T17:26:56.346369Z","submitted_at":"2022-01-21T05:49:15Z","title":"Fair Node Representation Learning via Adaptive Data Augmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.08549","snapshot_observed_at":"2026-08-15T16:56:19.718005Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19547","last_updated":"2025-08-27T03:41:01Z","snapshot_observed_at":"2026-08-15T16:48:15.654506Z","submitted_at":"2025-08-27T03:41:01Z","title":"Improving Recommendation Fairness via Graph Structure and Representation Augmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:19.718005Z"},"links":{"cited_paper":"/paper/2201.08549","citing_paper":"/paper/2508.19547"},"observation_digest":"sha256:a71921597be341ff707ff041ec27177dc28579761307b0c13675391a721eddc1","observation_id":"aefd7c09-4883-4ffb-91af-caa54e364109","resolution":{"observed_at":"2026-08-15T16:56:19.718005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2201.08549/citation-record","integrity":"/paper/2201.08549/integrity","json":"/paper/2201.08549/citation-record.json","paper":"/paper/2201.08549"},"outbound":[],"paper":{"arxiv_id":"2201.08549","last_updated":"2022-01-21T05:49:15Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T17:26:56.346369Z","submitted_at":"2022-01-21T05:49:15Z","title":"Fair Node Representation Learning via Adaptive Data Augmentation"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2201.08549."}