{"as_of":"2026-08-15T15:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9874dd6b50ce23511e7462924ca23e209f2e85bad32d227b17d3386f1d1a751f","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T23:14:03.455219Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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":"2026-08-12T15:54:48.951027Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15693","snapshot_observed_at":"2026-08-12T15:54:48.951027Z","title":"Hgformer: Hyperbolic graph transformer for recommendation","venue":null,"work_id":null,"year":2024},"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":40,"source":"pdf_text","source_observed_at":"2026-08-12T15:54:48.951027Z"},"links":{"cited_paper":"/paper/2502.15693","citing_paper":"/paper/2411.13865"},"observation_digest":"sha256:dd1215598badeaf9cc239ee7031804384d872bba90292f2046f2a246e9f7f516","observation_id":"3a4f7bde-4342-4a72-957d-3ef68bdeeac1","resolution":{"observed_at":"2026-08-12T15:54:48.951027Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"cited_work":{"arxiv_id":"2502.15693","doi":"10.48550/arxiv.2502.15693","metadata_source":"pith","pith_arxiv_id":"2502.15693","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","venue":"cs.IR","work_id":"759567f7-2422-4c5d-af3f-5c3ff318f29c","year":2024},"citing_paper":{"arxiv_id":"2510.07716","last_updated":"2026-06-24T21:14:37Z","snapshot_observed_at":"2026-08-15T12:34:45.126988Z","submitted_at":"2025-10-09T02:53:26Z","title":"Computationally-efficient Graph Modeling with Refined Graph Random Features","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-04T11:01:24.380190Z"},"links":{"cited_paper":"/paper/2502.15693","citing_paper":"/paper/2510.07716"},"observation_digest":"sha256:4e2e1ff41eb611917876db6399ccdee34eb3bfc6618d5aba8e87ffc82d5e448e","observation_id":"2700f96e-71c8-4192-9c6a-86254a5168e8","resolution":{"observed_at":"2026-08-04T11:04:50.658389Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/2502.15693/citation-record","integrity":"/paper/2502.15693/integrity","json":"/paper/2502.15693/citation-record.json","paper":"/paper/2502.15693"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.170030Z","title":"Wide & deep learning for recommender systems,","venue":null,"work_id":"05dd5106-471a-461b-91c9-966a4cc5bfea","year":2016},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.234573Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:fe103c824e84d5ef84d04a63639e1e609376a10cc89b9075c21a008c86f37cc1","observation_id":"117ea333-3c42-4e36-a5a9-53c6ab64e6f5","resolution":{"observed_at":"2026-08-10T23:14:04.175494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.154728Z","title":"The youtube video recommendation system,","venue":null,"work_id":"3a25b1aa-ee65-4e5e-aa23-0cffeca9a2f9","year":2010},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.240475Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:6df3deaae98a4877a8cbbc5ff1c36f4fef6c2c37ff3f3354761f080d487f767b","observation_id":"4e72b02f-08f1-4e64-a787-edc42a2dcea1","resolution":{"observed_at":"2026-08-10T23:14:04.159616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.138791Z","title":"Edgerec: recommender system on edge in mobile taobao,","venue":null,"work_id":"29605bb2-4c9a-4301-ba73-ff58cf70a56a","year":2020},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.245523Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:7e777ff7c8260a9c36eb9160179462333457803964f44e0c531611d0a903d8f7","observation_id":"5a110865-a225-485c-afcd-1aa2c31e3714","resolution":{"observed_at":"2026-08-10T23:14:04.144630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1205.2618","last_updated":"2012-05-09T18:25:09Z","snapshot_observed_at":"2026-08-15T03:23:25.220336Z","submitted_at":"2012-05-09T18:25:09Z","title":"BPR: Bayesian Personalized Ranking from Implicit Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1205.2618","snapshot_observed_at":"2026-08-10T23:14:03.250950Z","title":"Bpr: Bayesian personalized ranking from implicit feedback,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.250950Z"},"links":{"cited_paper":"/paper/1205.2618","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:71d0d663472c9d8a49b0622ffb2c044f46ad2e0e4098fe4f6adcfe19639ae7de","observation_id":"9262e38c-f351-4436-b17c-ce1e55090da3","resolution":{"observed_at":"2026-08-10T23:14:03.250950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.123371Z","title":"Deep matrix factorization models for recommender systems","venue":null,"work_id":"5fbc3aec-6f9d-4b7d-aca2-245d08e5f366","year":2017},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.257033Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:68e6b9d8ab32a545a77a051b37ca1a66388928dc298bf3c25987a3af92d3c4b1","observation_id":"399f1194-692b-4536-b3fa-64d72da8dc37","resolution":{"observed_at":"2026-08-10T23:14:04.128699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.108656Z","title":"Spectral collaborative filtering,","venue":null,"work_id":"e4d8de6c-a478-45bb-9e49-311f280eea3c","year":2018},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.262507Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:3c95c1067da1cbe5bd819748529615664d44f5c2d8d5b5d87ea12e756a2e76d8","observation_id":"56dc65e6-5534-4270-aac1-f34ccb106420","resolution":{"observed_at":"2026-08-10T23:14:04.113462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.091637Z","title":"LightGCN: Simplifying and powering graph convolution network for recommenda- tion,","venue":null,"work_id":"9f347c67-f35d-48f6-b4f5-19eace4affc1","year":2020},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.269159Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:b858196a793fd17ddcb9f1c9e12c789508ab532083d721a36ffe75fdf769a8a7","observation_id":"4dbaa91c-f7b2-4dff-8b11-f8fdc2870d42","resolution":{"observed_at":"2026-08-10T23:14:04.097028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.076187Z","title":"Neural graph collaborative filtering,","venue":null,"work_id":"623bc3ac-59cd-4dd7-a942-cbce5c30bd51","year":2019},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.274575Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:ec8cc2541aa2e9f83d0ef8d749a80001041348c70a67adcd4a838122c71c2eab","observation_id":"62746876-f786-475a-ab8c-5960c08b5bff","resolution":{"observed_at":"2026-08-10T23:14:04.081067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.061185Z","title":"Hgcf: Hyperbolic graph convolution networks for collaborative filtering,","venue":null,"work_id":"a9f07152-6076-41dd-b29b-e41edc568859","year":2021},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.279969Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:c850d5f97c37124261f41521d58e252bd8b9bc5525ec54ad3c105a7bf3823d66","observation_id":"f832b18e-c45a-4022-a157-407434ec5fb2","resolution":{"observed_at":"2026-08-10T23:14:04.065953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-13T11:38:10.906031Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-10T23:14:03.285051Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.285051Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:13f5a783acc8a0baedeacb779a4749a8df756cf215e8b9ad3dbe5832cb8d3d80","observation_id":"b0a60710-7719-4171-baa9-3e8783998cfb","resolution":{"observed_at":"2026-08-10T23:14:03.285051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.046245Z","title":"Inductive representation learning on large graphs,","venue":null,"work_id":"f36cc1bf-79c5-4e39-a199-a3752c9a0d73","year":2017},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.290328Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:f9a714324d65ced8a2653e949e71cb3051e6345878ce748c2c04d43a70d9bc99","observation_id":"24ca4ffb-07a3-4313-93ff-23bf4132ad37","resolution":{"observed_at":"2026-08-10T23:14:04.051184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.031023Z","title":"The long tail of recommender systems and how to leverage it,","venue":null,"work_id":"4176beba-7960-44a8-9ccf-c6f35b2441af","year":2008},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.295675Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:eb6e5b04a317a070e5a2cb1adaae0ae66d6d14f6a75145d6200ac43f367e15d8","observation_id":"ffb49d8e-cd0b-4e2a-bddc-7f1da5657d61","resolution":{"observed_at":"2026-08-10T23:14:04.036011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:04.015297Z","title":"The adaptive clustering method for the long tail problem of recommender systems,","venue":null,"work_id":"6c56c415-ef79-4ae1-9dee-ce036d8eecb7","year":1904},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.300861Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:5f42e0507896bbe6e7fc1ecc651fae18bc76fd8e2d4d970e7fa07f20fbe5dc4f","observation_id":"6ad73e2f-900e-40ec-a463-3e6a1541fb2d","resolution":{"observed_at":"2026-08-10T23:14:04.020399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1205.6700","last_updated":"2012-05-30T14:33:56Z","snapshot_observed_at":"2026-08-15T00:58:30.181769Z","submitted_at":"2012-05-30T14:33:56Z","title":"Challenging the Long Tail Recommendation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1205.6700","snapshot_observed_at":"2026-08-10T23:14:03.305673Z","title":"Challenging the long tail recommendation,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.305673Z"},"links":{"cited_paper":"/paper/1205.6700","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:3bc100f1603dd087e8da9dffda77ade6cac301b7fbe5f72859a48c8a094f5114","observation_id":"5887b5be-39ae-4087-a828-242c2eeb06e3","resolution":{"observed_at":"2026-08-10T23:14:03.305673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.998864Z","title":"Alleviating the long-tail problem in conversational recommender systems,","venue":null,"work_id":"811c7575-f179-4786-a4fd-fc2190528696","year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.311011Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:46e7229011275809570f7a0263b9317f28ed8670c81c448d9e45f395fc4ba651","observation_id":"b5cefdf7-8e7f-4a5f-a248-97f0ce772fae","resolution":{"observed_at":"2026-08-10T23:14:04.004168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.982802Z","title":"Hicf: Hyperbolic informative collaborative filtering,","venue":null,"work_id":"7edfb950-ecc7-4035-9fa1-bbe4ec8d3e42","year":2022},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.315574Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:4c3768f4e47935f51ff7a2d4e5274253ec3b741f0314966f90803d6cb0764e75","observation_id":"d42d23ad-c606-4e00-91a8-636da33a0d6d","resolution":{"observed_at":"2026-08-10T23:14:03.987979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.967094Z","title":"On generalized degree fairness in graph neural networks,","venue":null,"work_id":"7d2ca09a-f388-41bb-9d5f-12e4cc5f3f59","year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.320466Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:c1ded35f7a40cb65171c02f58229e3ef7fc65891e40ecd0756d1817c50085642","observation_id":"082e302d-3ff8-4de3-a93d-8c1931f996f0","resolution":{"observed_at":"2026-08-10T23:14:03.972204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.949725Z","title":"Lte4g: Long-tail experts for graph neural networks,","venue":null,"work_id":"02caa3dd-dced-450d-b0f7-f9d368141109","year":2022},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.325210Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:cd88844e9dcd41f12e03209e3a0e2e5f254fa3155bb763066a0395e2f815e2a1","observation_id":"c4e98a14-b2f4-41a1-9f61-640780a709c4","resolution":{"observed_at":"2026-08-10T23:14:03.955431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.932895Z","title":"Do transformers really perform badly for graph representation?","venue":null,"work_id":"a246a9d4-5412-4a51-8e3f-7946bf5afe0e","year":2021},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.330886Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:e624c5cd47db4dac5f91d1a63ab9765352e683786419dc1dacd07c4694dfd31c","observation_id":"7ca068ba-1644-45d2-94d5-83db73e31a24","resolution":{"observed_at":"2026-08-10T23:14:03.937792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2301.09474","last_updated":"2023-05-28T09:19:49Z","snapshot_observed_at":"2026-08-13T12:59:35.404748Z","submitted_at":"2023-01-23T15:18:54Z","title":"DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.09474","snapshot_observed_at":"2026-08-10T23:14:03.337651Z","title":"Difformer: Scalable (graph) transformers induced by energy constrained diffusion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.337651Z"},"links":{"cited_paper":"/paper/2301.09474","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:c00d2e3cd0b3b430c2675d5fdf094f0eadf5a5168a2896eaa126fc48337f3573","observation_id":"9dae8015-7fdd-4dd2-ac08-a2ff2d0c02c0","resolution":{"observed_at":"2026-08-10T23:14:03.337651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.917021Z","title":"Simplifying and empowering transformers for large-graph representations,","venue":null,"work_id":"59ce023a-5c94-4193-85d2-20f1522874b0","year":2024},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.343355Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:a6308dd5d0e54501480e9e8f1013bb4f60bd536ef990ebca77bfd226635e3c21","observation_id":"b1d7298a-bc6d-40aa-8bef-6f13d9bd04fa","resolution":{"observed_at":"2026-08-10T23:14:03.922218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.900859Z","title":"Nodeformer: A scalable graph structure learning transformer for node classification,","venue":null,"work_id":"8aba4de5-58ee-454e-b0bc-dc67d05ed344","year":2022},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.348542Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:400c79625893236e0367c330418589bcfb4a846672ed1a8caebc7964dd5b6c36","observation_id":"0ded2887-50fa-4e0c-8b8f-cc1e3e3ac575","resolution":{"observed_at":"2026-08-10T23:14:03.905585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.884688Z","title":"Hierarchical organization in complex networks,","venue":null,"work_id":"710c9d09-6be1-4aa6-a7a3-b4f67cae7c5d","year":2003},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.353903Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:ecdd4873e4e46eb11b06236878e37de7fab21df97b1d7a2dd10eaaf84b1a7a8c","observation_id":"429f5f8e-d9a1-49b3-9f6f-83406e4de0a6","resolution":{"observed_at":"2026-08-10T23:14:03.889945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.868131Z","title":"Hyperbolic graph convo- lutional neural networks,","venue":null,"work_id":"4018f139-e351-48fe-bf67-317c34450314","year":2019},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.359729Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:46b08dd7bf0e912019b1aed60b2de1329cafcf62d01ebb95f8ca32a7a963a718","observation_id":"8265f4bc-d7ee-4352-b18e-405b6ebb4c5d","resolution":{"observed_at":"2026-08-10T23:14:03.873574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.365605Z","title":"Hyperbolic neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.365605Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:adfe7fe82a1af61b04c6de7b2e758b1cba721192b09004422aa6ec6f93de656b","observation_id":"0fb22d91-9208-403e-be34-02385731385a","resolution":{"observed_at":"2026-08-10T23:14:03.365605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02961","last_updated":"2024-12-12T15:21:44Z","snapshot_observed_at":"2026-08-13T12:08:11.028839Z","submitted_at":"2023-04-06T09:38:54Z","title":"HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation","version":2},"cited_work":{"arxiv_id":"2304.02961","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.02961","snapshot_observed_at":"2026-08-10T23:14:03.611927Z","title":"HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation","venue":"cs.IR","work_id":"cf9c6d51-5a96-4899-89f1-d400148c9b63","year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.371908Z"},"links":{"cited_paper":"/paper/2304.02961","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:2e977669158237ac10edd8788378e9bf0d3361af2dcffa8fe3ed19f5d64541cb","observation_id":"29c02d8e-ed47-4ec6-b30a-72e360e7b7e5","resolution":{"observed_at":"2026-08-10T23:14:03.617662Z","resolver_source":"local_arxiv","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":"2302.01409","last_updated":"2023-02-02T20:47:45Z","snapshot_observed_at":"2026-08-13T12:52:26.534739Z","submitted_at":"2023-02-02T20:47:45Z","title":"Hyperbolic Contrastive Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.01409","snapshot_observed_at":"2026-08-10T23:14:03.378223Z","title":"Hyperbolic contrastive learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.378223Z"},"links":{"cited_paper":"/paper/2302.01409","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:e3b4e71a640bae1e4c431bdff6231725675905861280d43d7c1e0bce4dd297f9","observation_id":"1db9ccb5-79cc-4a0f-b903-d5be29f7d470","resolution":{"observed_at":"2026-08-10T23:14:03.378223Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.841827Z","title":"Hrcf: Enhancing collab- orative filtering via hyperbolic geometric regularization,","venue":null,"work_id":"1792af49-4400-479a-ac3a-8d6e3d2014d1","year":2022},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.384354Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:f35993b2b53c7050f00e0e9324b4f350a06fd99223cd2df29c45fc2ccb3510d4","observation_id":"fbf3ab01-8a67-47fa-b6f4-a7bdae9fa237","resolution":{"observed_at":"2026-08-10T23:14:03.847226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.824510Z","title":"Hyperbolic graph attention network,","venue":null,"work_id":"4bc10f51-f425-4d30-99df-bee20118d782","year":2021},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.389311Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:3e172efccb60b47134dca7fde8b37e4c9c870eee7756df073f4d4634c1ea5967","observation_id":"85ebfad6-6e7d-4377-97bb-4e08957c540a","resolution":{"observed_at":"2026-08-10T23:14:03.829537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2407.01290","last_updated":"2025-08-24T12:17:27Z","snapshot_observed_at":"2026-08-12T23:31:16.465725Z","submitted_at":"2024-07-01T13:44:38Z","title":"Hypformer: Exploring Efficient Transformer Fully in Hyperbolic Space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01290","snapshot_observed_at":"2026-08-10T23:14:03.394074Z","title":"Hypformer: Exploring efficient hyperbolic transformer fully in hyperbolic space,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.394074Z"},"links":{"cited_paper":"/paper/2407.01290","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:4a873c4b18d98d014d84338aee031d215cb4dfd24d6dbb25719fc2a9423f8eaf","observation_id":"6c524327-4db3-4d10-8517-b2dccb673ff6","resolution":{"observed_at":"2026-08-10T23:14:03.394074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.15919","last_updated":"2024-02-07T13:46:35Z","snapshot_observed_at":"2026-08-15T03:50:58.580840Z","submitted_at":"2023-03-28T12:20:52Z","title":"Fully Hyperbolic Convolutional Neural Networks for Computer Vision","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.15919","snapshot_observed_at":"2026-08-10T23:14:03.399176Z","title":"Fully hyperbolic convolutional neural networks for computer vision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.399176Z"},"links":{"cited_paper":"/paper/2303.15919","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:f92329d3c0215b86875d6664be6826d6afd991ba2336e82ca68724fb2c3b478c","observation_id":"3ce99da8-6f7d-4ed8-8b7e-11c4d4c103fa","resolution":{"observed_at":"2026-08-10T23:14:03.399176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.404293Z","title":"Boumal, An introduction to optimization on smooth manifolds","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.404293Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:0de415fd0599559656234b4faa066c38b5e12ce86566e85a62e8a658f7e8cf58","observation_id":"62aa3b77-60e4-4a7b-8b0c-d10a7d4098d1","resolution":{"observed_at":"2026-08-10T23:14:03.404293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.797740Z","title":"Neighbour interaction based click-through rate prediction via graph-masked transformer,","venue":null,"work_id":"8dcc272a-dd98-4804-97b5-f7b0cae3a1f2","year":2022},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.409127Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:c0c71216d25484f42694ee4d4671bcbfcf602307b594707886d20705a3cfa564","observation_id":"9bf23474-7ed0-4dec-a8c9-9e0ffd7467a4","resolution":{"observed_at":"2026-08-10T23:14:03.802953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.779968Z","title":"Linear, or non-linear, that is the question!","venue":null,"work_id":"a87d45ef-7676-46fc-96be-270d8b743f44","year":2022},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.414129Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:f63d42f68dd66d27881559a8dde1eea7da47e7d4e9764d295b1f3450844068aa","observation_id":"6cb3beaf-0876-44f8-a0ad-19fd781dfdd1","resolution":{"observed_at":"2026-08-10T23:14:03.785779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2105.14686","last_updated":"2022-03-16T02:23:49Z","snapshot_observed_at":"2026-08-13T19:13:36.462690Z","submitted_at":"2021-05-31T03:36:49Z","title":"Fully Hyperbolic Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.14686","snapshot_observed_at":"2026-08-10T23:14:03.419306Z","title":"Fully hyperbolic neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.419306Z"},"links":{"cited_paper":"/paper/2105.14686","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:fd3b883f59377a7193af1a23d913449f233d5413a1b7d8a73814d7815d687b34","observation_id":"084bde95-cda0-4998-a957-eac047821a00","resolution":{"observed_at":"2026-08-10T23:14:03.419306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.762425Z","title":"A hyperbolic-to-hyperbolic graph convolutional network,","venue":null,"work_id":"49e38f02-44f9-4c8c-92af-8529e266b7bb","year":2021},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.425180Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:368ece2b891cef2f23d72f3f77396d746e174293226bf0c11ebf4bd8d83fe965","observation_id":"ce0ae2fc-7c97-40f9-a948-bcd75e50509d","resolution":{"observed_at":"2026-08-10T23:14:03.767936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.745511Z","title":"Lorentzian graph convolutional networks,","venue":null,"work_id":"d20319e5-51bd-46a8-90a8-801a6ac45772","year":2021},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.430118Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:e5fd54dfc78c92d81c45fc7aef50340b8f956ded88d5e9d67c0ffbf2b6242c82","observation_id":"ac37e385-0ec1-4cd8-9e9c-c7bf42bf7c27","resolution":{"observed_at":"2026-08-10T23:14:03.751184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2406.17289","last_updated":"2024-07-04T14:54:07Z","snapshot_observed_at":"2026-08-12T23:35:33.901277Z","submitted_at":"2024-06-25T05:35:02Z","title":"Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System","version":2},"cited_work":{"arxiv_id":"2406.17289","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.17289","snapshot_observed_at":"2026-08-10T23:14:03.520522Z","title":"Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System","venue":"cs.IR","work_id":"e86527ae-6ad7-4544-8bcf-1fa95dce7488","year":2024},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.434980Z"},"links":{"cited_paper":"/paper/2406.17289","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:5bad94911e01734b22a0d5436ac5c157add7078f2df03d8fb2fd516ceb7cabc8","observation_id":"3e6446ff-c866-42a4-b4ba-dd2fe5a02c1c","resolution":{"observed_at":"2026-08-10T23:14:03.526215Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.729520Z","title":"Graph transformer networks,","venue":null,"work_id":"ccdb8268-c347-4af4-983b-3094af161d0a","year":2019},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.439674Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:01b47567dfedd9bf16fbb34c2f90281bbc6271ce9b6eee9d5255f47abcb0d99f","observation_id":"8f16ab1b-2ed0-4f7c-bdb1-59c89c01303b","resolution":{"observed_at":"2026-08-10T23:14:03.734351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.713587Z","title":"Graph transformer for recommendation,","venue":null,"work_id":"b488463a-5908-4b03-b640-75092956dbbe","year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.444935Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:8bcdda941d7d3613e6deef079a2f0fe3e22e67023a0507498202d2535290a8da","observation_id":"8aa6ff4c-fc15-4a65-b251-d39a28f30dd1","resolution":{"observed_at":"2026-08-10T23:14:03.718810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2404.11982","last_updated":"2024-05-06T11:28:15Z","snapshot_observed_at":"2026-08-13T00:27:37.000204Z","submitted_at":"2024-04-18T08:26:04Z","title":"SIGformer: Sign-aware Graph Transformer for Recommendation","version":3},"cited_work":{"arxiv_id":"2404.11982","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.11982","snapshot_observed_at":"2026-08-10T23:14:03.494667Z","title":"SIGformer: Sign-aware Graph Transformer for Recommendation","venue":"cs.IR","work_id":"676be03e-0340-499f-b71d-0adb6b458310","year":2024},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.449744Z"},"links":{"cited_paper":"/paper/2404.11982","citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:07bacdd04d228d6e29dcb917ddcc5cba8cac9c84676ede177e17835caed40411","observation_id":"2c5dae7f-baa7-4ecd-9099-b6638df102db","resolution":{"observed_at":"2026-08-10T23:14:03.502413Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:14:03.696810Z","title":"Lightgt: A light graph transformer for multimedia recommendation,","venue":null,"work_id":"6fa90a28-ca18-4447-80cd-6903705c559f","year":2023},"citing_paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T23:14:03.455219Z"},"links":{"citing_paper":"/paper/2502.15693"},"observation_digest":"sha256:04dfb7692161699b2f99744fbeed23b9d016e9b22b0ca3a769c93978dbd74a95","observation_id":"6efccf07-47ca-45d2-8da5-fd8d28c4d194","resolution":{"observed_at":"2026-08-10T23:14:03.701995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2502.15693","last_updated":"2024-12-30T10:50:51Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-15T12:34:00.202780Z","submitted_at":"2024-12-30T10:50:51Z","title":"Hgformer: Hyperbolic Graph Transformer for Recommendation"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":3,"verified_fuzzy":29},"total_outbound_references":42},"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 42 of 42 outbound references and 2 inbound Pith citation observations for arXiv:2502.15693."}