{"as_of":"2026-08-08T19:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6fec3c5a7b1590b8082dd8a48e959f76b5232040600feb416bd6e564d495d6ff","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:18:43.470558Z","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":"2026-08-07T10:19:09.779824Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1809.02589","last_updated":"2019-05-22T13:48:41Z","snapshot_observed_at":"2026-07-06T06:59:52.208971Z","submitted_at":"2018-09-07T17:27:25Z","title":"HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs","version":4},"cited_work":{"arxiv_id":"1809.02589","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.02589","snapshot_observed_at":"2026-08-07T10:19:09.779824Z","title":"HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs","venue":"cs.LG","work_id":"da6982d3-1e4f-4de3-9e3a-8047ece35425","year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:43.470558Z"},"links":{"cited_paper":"/paper/1809.02589","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:4028c35c8289c2a674688f3ea39b01b1117de51c3d5cca0c817fce1e2866822c","observation_id":"0ca4d192-cc5d-43c2-9228-c0b640c3f9b5","resolution":{"observed_at":"2026-08-07T10:19:09.846316Z","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/1809.02589/citation-record","integrity":"/paper/1809.02589/integrity","json":"/paper/1809.02589/citation-record.json","paper":"/paper/1809.02589"},"outbound":[],"paper":{"arxiv_id":"1809.02589","last_updated":"2019-05-22T13:48:41Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T06:59:52.208971Z","submitted_at":"2018-09-07T17:27:25Z","title":"HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs"},"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 1 inbound Pith citation observation for arXiv:1809.02589."}