{"as_of":"2026-08-07T11:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f234194976e5fedf189e5b94ed9e1e1dccc1221fecf2590e82649022202ff8a0","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":"2026-08-07T06:34:17.273281+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":"2026-08-07T05:00:55.460785Z","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":"2026-08-07T05:00:55.740449Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1611.08097","last_updated":"2017-05-03T12:37:19Z","snapshot_observed_at":"2026-08-06T08:20:55.937856Z","submitted_at":"2016-11-24T08:45:01Z","title":"Geometric deep learning: going beyond Euclidean data","version":2},"cited_work":{"arxiv_id":"1611.08097","doi":null,"metadata_source":"pith","pith_arxiv_id":"1611.08097","snapshot_observed_at":"2026-08-07T05:00:55.740449Z","title":"Geometric deep learning: going beyond Euclidean data","venue":"cs.CV","work_id":"e0975a48-3af4-450b-ba23-244305d6e9ca","year":2016},"citing_paper":{"arxiv_id":"2506.09234","last_updated":"2025-06-10T20:42:41Z","snapshot_observed_at":"2026-08-07T04:51:22.826987Z","submitted_at":"2025-06-10T20:42:41Z","title":"Transaction Categorization with Relational Deep Learning in QuickBooks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:00:55.460785Z"},"links":{"cited_paper":"/paper/1611.08097","citing_paper":"/paper/2506.09234"},"observation_digest":"sha256:dc475704898a91704f25594938d2619255a71648e37dde26c2e2023e7a23089d","observation_id":"5c98666c-a9da-4cfb-a053-ea656354936c","resolution":{"observed_at":"2026-08-07T05:00:55.744055Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.08097","last_updated":"2017-05-03T12:37:19Z","snapshot_observed_at":"2026-08-06T08:20:55.937856Z","submitted_at":"2016-11-24T08:45:01Z","title":"Geometric deep learning: going beyond Euclidean data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.08097","snapshot_observed_at":"2026-07-14T00:37:04.989965Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.10074","last_updated":"2026-07-11T01:59:00Z","snapshot_observed_at":"2026-08-06T13:32:38.188599Z","submitted_at":"2026-07-11T01:59:00Z","title":"Distance-Preserving Embeddings in Inhomogeneous Random Graphs","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-07-14T00:37:04.989965Z"},"links":{"cited_paper":"/paper/1611.08097","citing_paper":"/paper/2607.10074"},"observation_digest":"sha256:5af3bd22a17b9fddfc0ce1df869e8e106df19c0d555f02d38f299291886f5ed6","observation_id":"ccac7e39-8932-4a4a-9777-6eb43c141b3f","resolution":{"observed_at":"2026-07-14T00:37:04.989965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1611.08097/citation-record","integrity":"/paper/1611.08097/integrity","json":"/paper/1611.08097/citation-record.json","paper":"/paper/1611.08097"},"outbound":[],"paper":{"arxiv_id":"1611.08097","last_updated":"2017-05-03T12:37:19Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T08:20:55.937856Z","submitted_at":"2016-11-24T08:45:01Z","title":"Geometric deep learning: going beyond Euclidean data"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1611.08097."}