{"as_of":"2026-08-15T21:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2ba350c8e9ae0516e93ea683f61f6463d36bc551dd19b75634a40483e307d1ce","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T04:15:50.876309Z","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":"2026-05-15T17:31:22.336712Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-08-12T15:54:48.759737Z","title":"Lightgcl: Simple yet effective graph contrastive learning for recommendation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.13865","last_updated":"2026-05-29T13:45:23Z","snapshot_observed_at":"2026-08-15T12:34:41.948946Z","submitted_at":"2024-11-21T06:01:47Z","title":"Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T15:54:48.759737Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2411.13865"},"observation_digest":"sha256:904232cf5c1dfa6697bf8fb9dc761718c35730ba8514da3035e24186466ddf0c","observation_id":"29e8a186-db65-47e7-8984-389c920f1375","resolution":{"observed_at":"2026-08-12T15:54:48.759737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-08-10T14:16:56.765829Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.15555","last_updated":"2025-01-26T15:07:52Z","snapshot_observed_at":"2026-08-14T11:30:42.695783Z","submitted_at":"2025-01-26T15:07:52Z","title":"Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T14:16:56.765829Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2501.15555"},"observation_digest":"sha256:57f35e5ad98bc863e3febd6c9a5e201effd3d999e791d25696079dc7c668927b","observation_id":"e5f993c7-c9c7-44c4-ba18-56a2ecbb7626","resolution":{"observed_at":"2026-08-10T14:16:56.765829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-08-07T13:08:13.434939Z","title":"”LightGCL: Simple yet effective graph contrastive learning for recom- mendation.” arXiv preprint arXiv:2302.08191 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00048","last_updated":"2025-05-28T17:21:41Z","snapshot_observed_at":"2026-08-13T14:30:25.269923Z","submitted_at":"2025-05-28T17:21:41Z","title":"Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:08:13.434939Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2506.00048"},"observation_digest":"sha256:e87c6973c3e6fce44118848bb6ed796d3fcfaed399964a07b62b4a6a6be04c97","observation_id":"a18119dc-0217-46f8-8a45-5fe468b44b9b","resolution":{"observed_at":"2026-08-07T13:08:13.434939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-08-05T05:40:35.203703Z","title":"Lightgcl: Simple yet effective graph contrastive learning for recommendation.arXiv preprint arXiv:2302.08191, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.05115","last_updated":"2025-09-05T13:57:07Z","snapshot_observed_at":"2026-08-10T00:52:03.832121Z","submitted_at":"2025-09-05T13:57:07Z","title":"Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T05:40:35.203703Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2509.05115"},"observation_digest":"sha256:50ee3f9e59a83954951dc526c587a53bf66765f1bffeb4a388ae50dd9e26eb08","observation_id":"2545efb5-5003-4c84-a231-bd867e438d4b","resolution":{"observed_at":"2026-08-05T05:40:35.203703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":"2302.08191","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lightgcl: Simple yet effective graph contrastive learning for recommendation","venue":null,"work_id":"919c0188-e09d-428e-bc9b-f23acdd0230c","year":2023},"citing_paper":{"arxiv_id":"2604.04530","last_updated":"2026-04-06T08:49:46Z","snapshot_observed_at":"2026-08-11T19:16:23.475795Z","submitted_at":"2026-04-06T08:49:46Z","title":"SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T20:23:28.021304Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2604.04530"},"observation_digest":"sha256:f676b700c56d710d367f8f64c7adc62a574e5e08a7aa27a274c22c4d1ee04b24","observation_id":"345fc6f0-43a9-4647-a16c-daf3fbb50a69","resolution":{"observed_at":"2026-05-10T22:00:47.673295Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":"2302.08191","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lightgcl: Simple yet effective graph contrastive learning for recommendation","venue":null,"work_id":"919c0188-e09d-428e-bc9b-f23acdd0230c","year":2023},"citing_paper":{"arxiv_id":"2604.15699","last_updated":"2026-07-19T02:35:09Z","snapshot_observed_at":"2026-08-10T21:04:14.066121Z","submitted_at":"2026-04-17T04:58:25Z","title":"Frequency-Corrupt Based Graph Self-Supervised Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T08:32:08.154085Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2604.15699"},"observation_digest":"sha256:d785ecc390b18156c2692978c35622d7eeff28205e1ce1015b3a72576df133db","observation_id":"8a1f2278-911f-4a4d-a1d5-5901c70255ab","resolution":{"observed_at":"2026-05-10T08:32:52.069339Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-08-02T16:10:59.602230Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.15699","last_updated":"2026-07-19T02:35:09Z","snapshot_observed_at":"2026-08-10T21:04:14.066121Z","submitted_at":"2026-04-17T04:58:25Z","title":"Frequency-Corrupt Based Graph Self-Supervised Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T16:10:59.602230Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2604.15699"},"observation_digest":"sha256:42e05b7c970ae698cad3e66b5ce97a0c022ca972b555c2f8677dfa1ecffd0c6e","observation_id":"1b83affa-f681-433e-87d0-72b27f657631","resolution":{"observed_at":"2026-08-02T16:10:59.602230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":"2302.08191","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lightgcl: Simple yet effective graph contrastive learning for recommendation","venue":null,"work_id":"919c0188-e09d-428e-bc9b-f23acdd0230c","year":2023},"citing_paper":{"arxiv_id":"2604.20858","last_updated":"2026-03-01T23:20:48Z","snapshot_observed_at":"2026-08-11T21:18:46.381745Z","submitted_at":"2026-03-01T23:20:48Z","title":"Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-15T17:30:44.919870Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2604.20858"},"observation_digest":"sha256:5ece82e5b0bb8ffd4e86c3a7c9d7122b3d0af786460637569a19d868ddb81490","observation_id":"04069419-e048-480f-bf48-6d056dea7837","resolution":{"observed_at":"2026-05-15T17:31:22.339816Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-08-03T00:40:02.982835Z","title":"arXiv preprint arXiv:2302.08191 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28708","last_updated":"2026-07-30T16:06:38Z","snapshot_observed_at":"2026-08-14T02:07:58.036816Z","submitted_at":"2026-07-30T16:06:38Z","title":"MMFGU: Multimodal Federated Graph Unlearning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-03T00:40:02.982835Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2607.28708"},"observation_digest":"sha256:278816a940c14b184b2b59389105de87916293700855d34e957f9a080f576f70","observation_id":"f8d84215-0b71-4c6f-9d6d-ddd6d90a74fa","resolution":{"observed_at":"2026-08-03T00:40:02.982835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.08191","snapshot_observed_at":"2026-08-14T04:15:50.876309Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.10247","last_updated":"2026-08-10T21:28:37Z","snapshot_observed_at":"2026-08-15T18:47:11.650163Z","submitted_at":"2026-08-10T21:28:37Z","title":"DualSpectralCF: Training-Free Sign-Aware Spectral Collaborative Filtering","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T04:15:50.876309Z"},"links":{"cited_paper":"/paper/2302.08191","citing_paper":"/paper/2608.10247"},"observation_digest":"sha256:6af313816f8cecb12d21b03bed6398f0cba0631bca7e9523f1f3080eb09b4d8f","observation_id":"3d532fa3-6f39-4995-a5c3-c4835ae5665a","resolution":{"observed_at":"2026-08-14T04:15:50.876309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2302.08191/citation-record","integrity":"/paper/2302.08191/integrity","json":"/paper/2302.08191/citation-record.json","paper":"/paper/2302.08191"},"outbound":[],"paper":{"arxiv_id":"2302.08191","last_updated":"2023-06-14T14:25:15Z","latest_version":3,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-13T17:22:58.348387Z","submitted_at":"2023-02-16T10:16:21Z","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2302.08191."}