{"as_of":"2026-08-14T17:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:90fd77530e494fae4cf9c023e242a7d17fd9515406d99a9ec63116eac02b437c","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":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":18,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T15:55:27.801353Z","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":"2026-07-04T09:49:45.013746Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-12T15:55:27.801353Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.13821","last_updated":"2024-11-27T06:12:01Z","snapshot_observed_at":"2026-08-13T16:28:28.916544Z","submitted_at":"2024-11-21T03:59:07Z","title":"Heterophilic Graph Neural Networks Optimization with Causal Message-passing","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T15:55:27.801353Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2411.13821"},"observation_digest":"sha256:4cc62074ef3a19332aea3ff035c46f13d1212203c6f85833178f64a8bb2950b6","observation_id":"dd3bfa9c-e5f5-45e8-8e70-4cb903c6654b","resolution":{"observed_at":"2026-08-12T15:55:27.801353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-11T18:13:12.670722Z","title":"Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08174","last_updated":"2025-06-01T19:26:04Z","snapshot_observed_at":"2026-08-13T18:02:19.514310Z","submitted_at":"2024-12-11T08:03:35Z","title":"Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T18:13:12.670722Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2412.08174"},"observation_digest":"sha256:35e73f3159de79f66bff462ef4df11d947b515b8477c5a21a61a04979efb10d9","observation_id":"36454f4a-bf82-4831-a006-b2033694eac3","resolution":{"observed_at":"2026-08-11T18:13:12.670722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-08T15:53:24.169189Z","title":"Graph prompt learning: A comprehensive survey and beyond,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.06327","last_updated":"2025-02-10T10:28:11Z","snapshot_observed_at":"2026-08-11T03:01:10.139558Z","submitted_at":"2025-02-10T10:28:11Z","title":"Prompt-Driven Continual Graph Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T15:53:24.169189Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2502.06327"},"observation_digest":"sha256:e118b80a5c23bbd7ac049db242b45259d3d90c574c33302ed89381590b77b768","observation_id":"ab1e0326-d98c-4cfb-b2e8-ba3e4dc4eea4","resolution":{"observed_at":"2026-08-08T15:53:24.169189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-07T15:00:24.447922Z","title":"All in one: Multi-task prompting for graph neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16860","last_updated":"2025-05-22T16:19:19Z","snapshot_observed_at":"2026-08-10T10:11:37.902918Z","submitted_at":"2025-05-22T16:19:19Z","title":"GCAL: Adapting Graph Models to Evolving Domain Shifts","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:00:24.447922Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2505.16860"},"observation_digest":"sha256:38a06311f2b750d2e606267405a2bf40b585d75486f5edfe99aa22f8fc86b5ef","observation_id":"3f5f9ef4-3afe-4efa-be06-1f2fb0535dfb","resolution":{"observed_at":"2026-08-07T15:00:24.447922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-07T05:17:17.126927Z","title":"arXiv preprint arXiv:2311.16534 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08326","last_updated":"2025-06-10T01:27:19Z","snapshot_observed_at":"2026-08-14T17:39:02.979064Z","submitted_at":"2025-06-10T01:27:19Z","title":"Graph Prompting for Graph Learning Models: Recent Advances and Future Directions","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T05:17:17.126927Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2506.08326"},"observation_digest":"sha256:1b2354d47da1c4317d70ecfc74e0d806255021c4b5157c630b932d451e217835","observation_id":"591c02fa-5ebe-44d8-9e14-971fd8699106","resolution":{"observed_at":"2026-08-07T05:17:17.126927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":"2311.16534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-07-04T09:49:45.013746Z","title":"Graph prompt learn- ing: A comprehensive survey and beyond.arXiv preprint arXiv:2311.16534","venue":null,"work_id":"e1eef284-694a-4de7-a1ba-107d15700353","year":2023},"citing_paper":{"arxiv_id":"2507.05311","last_updated":"2026-05-22T09:31:03Z","snapshot_observed_at":"2026-08-13T19:58:27.654957Z","submitted_at":"2025-07-07T09:48:09Z","title":"PLACE: Prompt Learning for Attributed Community Search in Large Graphs","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-25T07:47:47.529586Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2507.05311"},"observation_digest":"sha256:0d62c81d1cbf5075ec7758e7711c140dcff661bc96406a1d36fd0b13535036c7","observation_id":"d707f116-be60-4020-9456-8556bdc6f451","resolution":{"observed_at":"2026-05-25T07:50:29.419177Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-06T18:49:12.732082Z","title":"Graph prompt learn- ing: A comprehensive survey and beyond","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.07405","last_updated":"2025-07-10T04:01:47Z","snapshot_observed_at":"2026-08-10T12:43:24.063596Z","submitted_at":"2025-07-10T04:01:47Z","title":"HGMP:Heterogeneous Graph Multi-Task Prompt Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T18:49:12.732082Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2507.07405"},"observation_digest":"sha256:5929dc93f7d8e8e324242d2214e3d77df072b051db77608aec4fe80ff84b7759","observation_id":"85e6406d-3474-45da-89ca-9e3fffd10a0f","resolution":{"observed_at":"2026-08-06T18:49:12.732082Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-06T18:12:27.890230Z","title":"Graph prompt learning: A comprehensive survey and beyond,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09132","last_updated":"2025-07-12T04:12:24Z","snapshot_observed_at":"2026-08-09T16:56:55.140311Z","submitted_at":"2025-07-12T04:12:24Z","title":"Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T18:12:27.890230Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2507.09132"},"observation_digest":"sha256:4e99c7dc1e3ebf65db7edff0376e0583980db99b0ddbb2eb7a47c3fe07a5aeef","observation_id":"487cfac6-7c28-424d-ab96-3750adfc5445","resolution":{"observed_at":"2026-08-06T18:12:27.890230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-05T14:36:36.444988Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.06975","last_updated":"2025-08-28T19:13:10Z","snapshot_observed_at":"2026-08-07T21:39:28.054869Z","submitted_at":"2025-08-28T19:13:10Z","title":"GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T14:36:36.444988Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2509.06975"},"observation_digest":"sha256:7561585588c6723433faa59740e096e60daab0794c72192b254bcd9ec855ed3e","observation_id":"031daf69-dc85-4349-a71c-ecd3f5a399e9","resolution":{"observed_at":"2026-08-05T14:36:36.444988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-04T11:30:51.929298Z","title":"Graph prompt learning: A comprehensive survey and beyond","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.04567","last_updated":"2026-06-10T08:33:20Z","snapshot_observed_at":"2026-08-13T21:05:01.853962Z","submitted_at":"2025-10-06T08:09:15Z","title":"GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-04T11:30:51.929298Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2510.04567"},"observation_digest":"sha256:8eff3ff19482f7c6387beb4de6d61caeba4a0f89f43bfad98345bd9ebdb8fa94","observation_id":"b254c64c-2c88-4db6-b229-4f2fac2a3743","resolution":{"observed_at":"2026-08-04T11:30:51.929298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":"2311.16534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-07-04T09:49:45.013746Z","title":"Graph prompt learn- ing: A comprehensive survey and beyond.arXiv preprint arXiv:2311.16534","venue":null,"work_id":"e1eef284-694a-4de7-a1ba-107d15700353","year":2023},"citing_paper":{"arxiv_id":"2510.22555","last_updated":"2026-05-05T03:00:23Z","snapshot_observed_at":"2026-08-04T23:07:25.119627Z","submitted_at":"2025-10-26T07:10:07Z","title":"Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-18T04:51:23.281272Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2510.22555"},"observation_digest":"sha256:266373e569fb86087f83109a01e030290ddd6b677ff2f65266c2f3596bbde8b9","observation_id":"6d4d458c-d635-46e5-b11f-792ac7fb0ce3","resolution":{"observed_at":"2026-05-18T04:52:23.404069Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-04T07:55:00.479053Z","title":"All in one: Multi-task prompting for graph neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.23469","last_updated":"2026-06-02T15:31:54Z","snapshot_observed_at":"2026-08-13T23:00:40.601469Z","submitted_at":"2025-10-27T16:07:36Z","title":"Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-04T07:55:00.479053Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2510.23469"},"observation_digest":"sha256:e93a5c9567a74260bf71f21ba33ef5a4a59a8f48a3528f21b50df122150a8e59","observation_id":"d3b00fe2-c482-4d0f-aafc-994d2a015f66","resolution":{"observed_at":"2026-08-04T07:55:00.479053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-02T23:38:54.632538Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.13075","last_updated":"2026-05-27T04:21:31Z","snapshot_observed_at":"2026-08-14T04:45:19.929038Z","submitted_at":"2026-02-13T16:34:55Z","title":"Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T23:38:54.632538Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2602.13075"},"observation_digest":"sha256:a80b00605b657833629a89c45473c50dd45c6046a2a3d06dc5b5b112b1cac135","observation_id":"df204f12-8368-4d0b-9e1f-920204e7a56c","resolution":{"observed_at":"2026-08-02T23:38:54.632538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":"2311.16534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-07-04T09:49:45.013746Z","title":"Graph prompt learn- ing: A comprehensive survey and beyond.arXiv preprint arXiv:2311.16534","venue":null,"work_id":"e1eef284-694a-4de7-a1ba-107d15700353","year":2023},"citing_paper":{"arxiv_id":"2604.11257","last_updated":"2026-04-13T10:07:12Z","snapshot_observed_at":"2026-07-06T22:59:40.900521Z","submitted_at":"2026-04-13T10:07:12Z","title":"Unified Graph Prompt Learning via Low-Rank Graph Message Prompting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T16:08:19.174713Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2604.11257"},"observation_digest":"sha256:0538d496d93ac1e01feb0b727780b1842fd1444ea87a08f60d53782eb8968d1e","observation_id":"da94785b-2f71-4059-bbac-ba8d7f342bb4","resolution":{"observed_at":"2026-05-11T09:16:02.714456Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":"2311.16534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-07-04T09:49:45.013746Z","title":"Graph prompt learn- ing: A comprehensive survey and beyond.arXiv preprint arXiv:2311.16534","venue":null,"work_id":"e1eef284-694a-4de7-a1ba-107d15700353","year":2023},"citing_paper":{"arxiv_id":"2605.15888","last_updated":"2026-06-05T02:17:47Z","snapshot_observed_at":"2026-08-02T15:57:41.366470Z","submitted_at":"2026-05-15T12:19:18Z","title":"CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-20T20:16:26.671005Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2605.15888"},"observation_digest":"sha256:243271c8b7010131928d2667f5e1870630a24a44d1f22b04e331ec94da2c45f7","observation_id":"fdc6d427-c212-49c3-8840-4f5bf40185a6","resolution":{"observed_at":"2026-05-20T20:18:59.776663Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":"2311.16534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-07-04T09:49:45.013746Z","title":"Graph prompt learn- ing: A comprehensive survey and beyond.arXiv preprint arXiv:2311.16534","venue":null,"work_id":"e1eef284-694a-4de7-a1ba-107d15700353","year":2023},"citing_paper":{"arxiv_id":"2605.15888","last_updated":"2026-06-05T02:17:47Z","snapshot_observed_at":"2026-08-02T15:57:41.366470Z","submitted_at":"2026-05-15T12:19:18Z","title":"CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-30T19:16:15.616715Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2605.15888"},"observation_digest":"sha256:2b6e4c9e8e2a524b8f1cb351ff0903ddb41b74a97cdc9bd069f2d9b93dade8a4","observation_id":"747dd362-cf0d-494b-86f2-b7a908cbc8fc","resolution":{"observed_at":"2026-07-01T14:55:47.461337Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":"2311.16534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-07-04T09:49:45.013746Z","title":"Graph prompt learn- ing: A comprehensive survey and beyond.arXiv preprint arXiv:2311.16534","venue":null,"work_id":"e1eef284-694a-4de7-a1ba-107d15700353","year":2023},"citing_paper":{"arxiv_id":"2606.22914","last_updated":"2026-06-22T06:54:02Z","snapshot_observed_at":"2026-08-08T13:39:39.335084Z","submitted_at":"2026-06-22T06:54:02Z","title":"PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T09:21:40.944884Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2606.22914"},"observation_digest":"sha256:3e591dbf64f994718f21ecd8d2d50ee19f125f158ec7612f90d72128fef7139f","observation_id":"dae729c2-6db4-4fd0-8aea-ffbfdcc4681a","resolution":{"observed_at":"2026-07-04T09:49:45.015075Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16534","snapshot_observed_at":"2026-08-02T14:39:19.424668Z","title":"Graph prompt learning: A comprehensive survey and beyond.arXiv preprint arXiv:2311.16534, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.14114","last_updated":"2026-05-08T08:31:18Z","snapshot_observed_at":"2026-08-10T12:44:43.445886Z","submitted_at":"2026-05-08T08:31:18Z","title":"CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T14:39:19.424668Z"},"links":{"cited_paper":"/paper/2311.16534","citing_paper":"/paper/2607.14114"},"observation_digest":"sha256:64673892b4afe4b7148e68e27b6eb64b73c6a950b9aff69cb34079ae88d15045","observation_id":"f527c140-6b00-429e-99a0-a128bc033320","resolution":{"observed_at":"2026-08-02T14:39:19.424668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2311.16534/citation-record","integrity":"/paper/2311.16534/integrity","json":"/paper/2311.16534/citation-record.json","paper":"/paper/2311.16534"},"outbound":[],"paper":{"arxiv_id":"2311.16534","last_updated":"2023-11-28T05:36:59Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-13T05:15:44.115558Z","submitted_at":"2023-11-28T05:36:59Z","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2311.16534."}