{"as_of":"2026-08-08T09:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:642868d7317260acb4465708415a8e12a1dc23ebb60d1eff2019896cc2cbec42","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":"2026-08-08T06:32:00.761636+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":"2026-08-06T16:23:00.090421Z","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":"2026-07-02T02:46:28.133847Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2409.05477","last_updated":"2024-09-18T09:15:10Z","snapshot_observed_at":"2026-07-31T22:01:54.302921Z","submitted_at":"2024-09-09T10:11:25Z","title":"Retrofitting Temporal Graph Neural Networks with Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.05477","snapshot_observed_at":"2026-08-06T16:23:00.090421Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13825","last_updated":"2025-07-18T11:29:15Z","snapshot_observed_at":"2026-08-06T16:13:17.543071Z","submitted_at":"2025-07-18T11:29:15Z","title":"When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:00.090421Z"},"links":{"cited_paper":"/paper/2409.05477","citing_paper":"/paper/2507.13825"},"observation_digest":"sha256:30ee08c98dcb718440ded31172b94726a599450b2dd1620b4c4fb93cb1abb960","observation_id":"90b14a50-104f-446f-ae3c-78ae28f641ed","resolution":{"observed_at":"2026-08-06T16:23:00.090421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.05477","last_updated":"2024-09-18T09:15:10Z","snapshot_observed_at":"2026-07-31T22:01:54.302921Z","submitted_at":"2024-09-09T10:11:25Z","title":"Retrofitting Temporal Graph Neural Networks with Transformer","version":3},"cited_work":{"arxiv_id":"2409.05477","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.05477","snapshot_observed_at":"2026-07-02T02:46:28.133847Z","title":"X., Han, Z., Fu, F., Zhang, W., & Jiang, J","venue":null,"work_id":"c497f674-6b6f-43d0-9511-77fd47c86147","year":2024},"citing_paper":{"arxiv_id":"2604.22781","last_updated":"2026-04-03T14:02:03Z","snapshot_observed_at":"2026-08-02T20:42:06.740243Z","submitted_at":"2026-04-03T14:02:03Z","title":"BiTA: Bidirectional Gated Recurrent Unit-Transformer Aggregator in a Temporal Graph Network Framework for Alert Prediction in Computer Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-13T20:00:30.175483Z"},"links":{"cited_paper":"/paper/2409.05477","citing_paper":"/paper/2604.22781"},"observation_digest":"sha256:3bcd1da3d1b0d528335124e4cb6c63194000c2b6d164f679c1070e37bff64843","observation_id":"fc709978-8aa7-4574-a286-d63ec09dabd0","resolution":{"observed_at":"2026-05-13T20:03:12.379817Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.05477","last_updated":"2024-09-18T09:15:10Z","snapshot_observed_at":"2026-07-31T22:01:54.302921Z","submitted_at":"2024-09-09T10:11:25Z","title":"Retrofitting Temporal Graph Neural Networks with Transformer","version":3},"cited_work":{"arxiv_id":"2409.05477","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.05477","snapshot_observed_at":"2026-07-02T02:46:28.133847Z","title":"X., Han, Z., Fu, F., Zhang, W., & Jiang, J","venue":null,"work_id":"c497f674-6b6f-43d0-9511-77fd47c86147","year":2024},"citing_paper":{"arxiv_id":"2606.03839","last_updated":"2026-06-02T16:20:02Z","snapshot_observed_at":"2026-08-06T16:16:03.956551Z","submitted_at":"2026-06-02T16:20:02Z","title":"Text-attributed Graph Condensation via Text Selection and Attribute Matching","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T10:42:08.372934Z"},"links":{"cited_paper":"/paper/2409.05477","citing_paper":"/paper/2606.03839"},"observation_digest":"sha256:bd54dea642ac312d4b62216b897f2acf341de9a54a4c5efa4b4720f3a6a97671","observation_id":"707cdd80-a0d8-4a37-ae28-336da9e62473","resolution":{"observed_at":"2026-07-02T02:46:28.136202Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.05477","last_updated":"2024-09-18T09:15:10Z","snapshot_observed_at":"2026-07-31T22:01:54.302921Z","submitted_at":"2024-09-09T10:11:25Z","title":"Retrofitting Temporal Graph Neural Networks with Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.05477","snapshot_observed_at":"2026-07-30T19:16:01.368996Z","title":"arXiv preprint arXiv:2409.05477 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23556","last_updated":"2026-07-26T09:15:27Z","snapshot_observed_at":"2026-08-07T06:20:35.435251Z","submitted_at":"2026-07-26T09:15:27Z","title":"GTIN: A Unified Framework for Joint Event and Time Prediction in Temporal Graphs","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-07-30T19:16:01.368996Z"},"links":{"cited_paper":"/paper/2409.05477","citing_paper":"/paper/2607.23556"},"observation_digest":"sha256:884dd576481574c6de45e6019f276c96bf563a4c1712a53d901c99e67374a663","observation_id":"f5f7611b-4ae9-4eee-808a-143237ee7d8c","resolution":{"observed_at":"2026-07-30T19:16:01.368996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2409.05477/citation-record","integrity":"/paper/2409.05477/integrity","json":"/paper/2409.05477/citation-record.json","paper":"/paper/2409.05477"},"outbound":[],"paper":{"arxiv_id":"2409.05477","last_updated":"2024-09-18T09:15:10Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-31T22:01:54.302921Z","submitted_at":"2024-09-09T10:11:25Z","title":"Retrofitting Temporal Graph Neural Networks with Transformer"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2409.05477."}