{"as_of":"2026-08-09T22:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0259fc57fbd76b4e66342edfceadfc5c1b8b6886d5b37b9a5134ab220d3d178d","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T22:15:29.396923Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T11:46:55.889503Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2102.10234","last_updated":"2021-02-20T02:51:13Z","snapshot_observed_at":"2026-07-06T10:43:02.182568Z","submitted_at":"2021-02-20T02:51:13Z","title":"Generalization bounds for graph convolutional neural networks via Rademacher complexity","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.10234","snapshot_observed_at":"2026-08-04T22:15:29.396923Z","title":"Generalization bounds for graph convolutional neural networks via rademacher complexity,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.07499","last_updated":"2025-09-09T08:25:11Z","snapshot_observed_at":"2026-08-08T23:22:16.224552Z","submitted_at":"2025-09-09T08:25:11Z","title":"Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-04T22:15:29.396923Z"},"links":{"cited_paper":"/paper/2102.10234","citing_paper":"/paper/2509.07499"},"observation_digest":"sha256:2697c347bf4a704d67715018d478946c8cdb00070c2d6bededffce8c2102f954","observation_id":"dfcb7397-dd14-469e-97e1-ec4b046fd8d9","resolution":{"observed_at":"2026-08-04T22:15:29.396923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.10234","last_updated":"2021-02-20T02:51:13Z","snapshot_observed_at":"2026-07-06T10:43:02.182568Z","submitted_at":"2021-02-20T02:51:13Z","title":"Generalization bounds for graph convolutional neural networks via Rademacher complexity","version":1},"cited_work":{"arxiv_id":"2102.10234","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.10234","snapshot_observed_at":"2026-07-02T11:46:55.889503Z","title":"arXiv preprint arXiv:2102.10234 , year=","venue":null,"work_id":"207507cc-481a-4dd7-a470-62f612b7c661","year":2021},"citing_paper":{"arxiv_id":"2604.10553","last_updated":"2026-04-12T09:52:27Z","snapshot_observed_at":"2026-07-06T22:59:09.778842Z","submitted_at":"2026-04-12T09:52:27Z","title":"Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T16:50:38.213750Z"},"links":{"cited_paper":"/paper/2102.10234","citing_paper":"/paper/2604.10553"},"observation_digest":"sha256:5dd88bb33534db3420d1322b1a9ae1e63dd276af96c1b2a6d20bf086644e13c6","observation_id":"51ee8957-0d99-4202-a201-552964280a87","resolution":{"observed_at":"2026-05-11T08:05:59.200543Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.10234","last_updated":"2021-02-20T02:51:13Z","snapshot_observed_at":"2026-07-06T10:43:02.182568Z","submitted_at":"2021-02-20T02:51:13Z","title":"Generalization bounds for graph convolutional neural networks via Rademacher complexity","version":1},"cited_work":{"arxiv_id":"2102.10234","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.10234","snapshot_observed_at":"2026-07-02T11:46:55.889503Z","title":"arXiv preprint arXiv:2102.10234 , year=","venue":null,"work_id":"207507cc-481a-4dd7-a470-62f612b7c661","year":2021},"citing_paper":{"arxiv_id":"2605.13597","last_updated":"2026-05-13T14:32:46Z","snapshot_observed_at":"2026-07-06T23:25:11.623026Z","submitted_at":"2026-05-13T14:32:46Z","title":"Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-14T19:44:40.720468Z"},"links":{"cited_paper":"/paper/2102.10234","citing_paper":"/paper/2605.13597"},"observation_digest":"sha256:62a748820a071355c7147655bf0829b68db3fd8e16a1a2fe73b29922e00e8bfb","observation_id":"4d2c5211-97f9-4838-ad07-9b4cd9d32e9e","resolution":{"observed_at":"2026-05-14T19:47:53.572792Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.10234","last_updated":"2021-02-20T02:51:13Z","snapshot_observed_at":"2026-07-06T10:43:02.182568Z","submitted_at":"2021-02-20T02:51:13Z","title":"Generalization bounds for graph convolutional neural networks via Rademacher complexity","version":1},"cited_work":{"arxiv_id":"2102.10234","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.10234","snapshot_observed_at":"2026-07-02T11:46:55.889503Z","title":"arXiv preprint arXiv:2102.10234 , year=","venue":null,"work_id":"207507cc-481a-4dd7-a470-62f612b7c661","year":2021},"citing_paper":{"arxiv_id":"2606.06293","last_updated":"2026-06-04T15:31:21Z","snapshot_observed_at":"2026-08-08T10:51:57.500235Z","submitted_at":"2026-06-04T15:31:21Z","title":"PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T02:58:52.293969Z"},"links":{"cited_paper":"/paper/2102.10234","citing_paper":"/paper/2606.06293"},"observation_digest":"sha256:c6ecac2b71b751729c703be50eb1d7271614d2a1134266c75d25c5caf0ce9cb6","observation_id":"9da708f2-5979-4fc7-a8f2-ca337408492d","resolution":{"observed_at":"2026-07-02T11:46:55.890927Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2102.10234/citation-record","integrity":"/paper/2102.10234/integrity","json":"/paper/2102.10234/citation-record.json","paper":"/paper/2102.10234"},"outbound":[],"paper":{"arxiv_id":"2102.10234","last_updated":"2021-02-20T02:51:13Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-07-06T10:43:02.182568Z","submitted_at":"2021-02-20T02:51:13Z","title":"Generalization bounds for graph convolutional neural networks via Rademacher complexity"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2102.10234."}