{"as_of":"2026-08-10T07:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:515192e97297edd5bd3ba714afb8ac6fa017e254b4ffa3da9af13758d2234387","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":"2026-08-10T06:31:04.303077+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":"2026-08-09T12:05:55.896131Z","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":"2026-08-09T12:05:56.069847Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.16670","last_updated":"2023-11-28T10:34:48Z","snapshot_observed_at":"2026-08-03T16:20:18.580483Z","submitted_at":"2023-11-28T10:34:48Z","title":"PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network","version":1},"cited_work":{"arxiv_id":"2311.16670","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.16670","snapshot_observed_at":"2026-08-09T12:05:56.069847Z","title":"PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network","venue":"cs.LG","work_id":"12e63bee-daf5-4012-b2de-1b2e6f05fcb6","year":2023},"citing_paper":{"arxiv_id":"2502.02479","last_updated":"2025-02-04T16:54:28Z","snapshot_observed_at":"2026-08-10T04:56:36.800403Z","submitted_at":"2025-02-04T16:54:28Z","title":"Using Random Noise Equivariantly to Boost Graph Neural Networks Universally","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-09T12:05:55.896131Z"},"links":{"cited_paper":"/paper/2311.16670","citing_paper":"/paper/2502.02479"},"observation_digest":"sha256:8850f798e1c2c41f0999d2dd929c83c817388218f6114058296696978200ce49","observation_id":"137bfce6-11ad-4c9a-a6cf-89845fe1b2ae","resolution":{"observed_at":"2026-08-09T12:05:56.078413Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2311.16670/citation-record","integrity":"/paper/2311.16670/integrity","json":"/paper/2311.16670/citation-record.json","paper":"/paper/2311.16670"},"outbound":[],"paper":{"arxiv_id":"2311.16670","last_updated":"2023-11-28T10:34:48Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T16:20:18.580483Z","submitted_at":"2023-11-28T10:34:48Z","title":"PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2311.16670."}