{"as_of":"2026-08-12T22:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a0d83bee7c90415ed63b58d2a0eed4a4fd25cf842a7da82565bd4aae62b73dd","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T11:56:36.819840Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T16:08:19.174713Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T09:16:02.750930Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"cited_work":{"arxiv_id":"2411.17676","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.17676","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Instance-aware graph prompt learning","venue":null,"work_id":"a5b60d69-5e97-4a6b-baca-9b436ce96cb9","year":2024},"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":17,"source":"pdf_text","source_observed_at":"2026-05-10T16:08:19.174713Z"},"links":{"cited_paper":"/paper/2411.17676","citing_paper":"/paper/2604.11257"},"observation_digest":"sha256:bdf15c8c627d4b3eb244cd3cf3611ad50df55042a043807d2615c27795b7d42f","observation_id":"7f92f6c7-83a2-4169-9217-228dd05d6367","resolution":{"observed_at":"2026-05-11T09:16:02.758050Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.17676/citation-record","integrity":"/paper/2411.17676/integrity","json":"/paper/2411.17676/citation-record.json","paper":"/paper/2411.17676"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:56:37.295274Z","title":"Extractive opinion summarization in quantized transformer spaces","venue":null,"work_id":"12e7c82a-dafc-4dfd-ad26-971d37b23d72","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.660711Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:7563a3080c687c413f50ce91a00d7a31f7eb8efc73101b4050591a8ceb7fcbb0","observation_id":"9ca82038-a810-4dd7-a92b-1f81c4966bbd","resolution":{"observed_at":"2026-08-12T11:56:37.298084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.285306Z","title":"Beit: Bert pre-training of image transformers","venue":null,"work_id":"21d01e9a-db35-4633-9696-ca1b9f1048ec","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.664278Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:87686e5fa7f71099c57be9a6819e4dcbd85a7a4f117280df25f92c32f862028e","observation_id":"29223f66-74e5-4200-9847-88e8bb2eba06","resolution":{"observed_at":"2026-08-12T11:56:37.288862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.276014Z","title":"Vector-quantized input-contextualized soft prompts for natural language understanding","venue":null,"work_id":"d4765d5c-25ea-4239-a55b-d4ac7a86a324","year":2022},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.667461Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:2a41cff0dd11698db2f61fab62036281dff89d7979c09829d6601ddfc83ef085","observation_id":"86c5739d-d435-477e-b1db-b01cd2f720be","resolution":{"observed_at":"2026-08-12T11:56:37.279352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.266996Z","title":"Enhancing graph neural network-based fraud detectors against camouflaged fraudsters","venue":null,"work_id":"9ac1258c-519c-4527-b589-92b94d3b36e5","year":2020},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.671083Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:e65713d0542d8cf6cc23bad9b82e117116772ebf799caa977f24efb7b70ce991","observation_id":"cf712884-c5fa-41ea-92b2-8a3da239d66a","resolution":{"observed_at":"2026-08-12T11:56:37.269863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.258112Z","title":"Multiscale vision transformers","venue":null,"work_id":"9b222f96-9fb3-4749-b3fa-17082a56bc7e","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.674280Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:982fcae0fe556ab7a59110045a446e70eece551865b2e70b041c34b88088f692","observation_id":"a5459a05-eb09-4a5f-8150-0ab6be20442e","resolution":{"observed_at":"2026-08-12T11:56:37.260847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.249642Z","title":"Graph neural networks for social recommendation","venue":null,"work_id":"e25c5960-1d98-42c8-b075-d8d10968d55a","year":2019},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.677568Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:b63dd17f0ca7114ba355c56fd46af65aa4cc71dc713552c35b3eb0023c2d93bf","observation_id":"d2c34d70-e8f5-407f-bdfd-02c6f94ea938","resolution":{"observed_at":"2026-08-12T11:56:37.253118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.240563Z","title":"Universal prompt tuning for graph neural networks","venue":null,"work_id":"ae0bb590-ab1c-412a-b854-50697759a4ac","year":2023},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.681746Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:11a87f9cba83a04ea1720d164038f8dc1ee4f865324a4ad75542149ade7e08e6","observation_id":"bc016e44-72a5-4a61-95fc-551503830447","resolution":{"observed_at":"2026-08-12T11:56:37.243957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.15723","last_updated":"2021-06-02T12:41:36Z","snapshot_observed_at":"2026-08-10T05:00:18.764813Z","submitted_at":"2020-12-31T17:21:26Z","title":"Making Pre-trained Language Models Better Few-shot Learners","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.15723","snapshot_observed_at":"2026-08-12T11:56:36.684286Z","title":"Making pre-trained language models better few-shot learners","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.684286Z"},"links":{"cited_paper":"/paper/2012.15723","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:45fd5fb1f2bf7a9228c73034ab578061a195afb812b73897d56a9087871aa68e","observation_id":"fd401f2a-0d9a-4a8b-9ebc-17e0596eb0be","resolution":{"observed_at":"2026-08-12T11:56:36.684286Z","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":"2026-08-12T11:56:37.231791Z","title":"Bellis, A","venue":null,"work_id":"7febe77e-daa8-4e61-aed7-5951d5a11ce3","year":2011},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.687696Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:60ce1f46c1a4cf9692360df800181fb050b45149eb67b4a9e304073a9659c9db","observation_id":"84019a22-8bbb-4e4e-835e-07942c5ca51f","resolution":{"observed_at":"2026-08-12T11:56:37.235067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.223016Z","title":"Quantization","venue":null,"work_id":"21b25175-e268-4e8e-8f52-a0859bf18e4e","year":1998},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.690803Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:5d3053d14909809896844c301361dc26a04bd0be1d09a8da54e0f94a53a339e8","observation_id":"40b17744-a06a-4e9a-9503-8363603a75ba","resolution":{"observed_at":"2026-08-12T11:56:37.226091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.213551Z","title":"node2vec: Scalable feature learning for networks","venue":null,"work_id":"f37f92e3-1fa7-4431-b718-07af85086b3e","year":2016},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.693408Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:a589fed3fb7f31adfbe0e33c62f72022c016a201d802705ceb59a71d3ddf0188","observation_id":"726611e7-3619-4823-b345-d40c559022a4","resolution":{"observed_at":"2026-08-12T11:56:37.216788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.04332","last_updated":"2022-03-14T02:05:06Z","snapshot_observed_at":"2026-08-09T18:32:47.872424Z","submitted_at":"2021-09-09T15:11:04Z","title":"PPT: Pre-trained Prompt Tuning for Few-shot Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.04332","snapshot_observed_at":"2026-08-12T11:56:36.696323Z","title":"Ppt: Pre-trained prompt tuning for few-shot learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.696323Z"},"links":{"cited_paper":"/paper/2109.04332","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:36096a1b2f1eb03240b4c5618560a941b1703eae3ae8236c9c4691ce7a49b771","observation_id":"8ad4ec56-45e5-40a5-a2a5-9f92d00d59bd","resolution":{"observed_at":"2026-08-12T11:56:36.696323Z","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":"2026-08-12T11:56:37.204178Z","title":"Few-shot graph learning for molecular property prediction","venue":null,"work_id":"26ee9d72-e603-4920-a2bb-6647d6f2216e","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.699357Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:9fe70120ab75cd8e81697ba2976c3059bf22488afe3fbc27427d915f3b6e1f60","observation_id":"514c3168-b214-4707-9ecd-6b63d9b3b691","resolution":{"observed_at":"2026-08-12T11:56:37.207142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.195393Z","title":"A deep graph neural network-based mechanism for social recommendations","venue":null,"work_id":"8cdab7a0-b35d-4bb2-a838-0eefbb1bad29","year":2020},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.702523Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:41e373b01b97360e2a60958b4e5eb15e88d61014a4548978a2975ebab61c446d","observation_id":"41ff7514-5442-4723-b00a-f0d521bffd04","resolution":{"observed_at":"2026-08-12T11:56:37.198460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.187361Z","title":"Inductive representation learning on large graphs","venue":null,"work_id":"638d3dce-ccba-4d7c-b3b3-ed9d4574de4d","year":2017},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.705840Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:f2c18604557d6a5551f563998e6ca73fe2b36f6b3e72d7343bb5757c322efc3b","observation_id":"575eaa4c-d0d3-4d7b-ab75-13de9d3e90cf","resolution":{"observed_at":"2026-08-12T11:56:37.190418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.178207Z","title":"Strategies for pre-training graph neural networks","venue":null,"work_id":"fae5966c-2ea2-4757-aaef-53be55fd1666","year":2020},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.708470Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:bf5adc8a5e8f18773545d44c6d0326e74ee17e110a456e6af87efef196e150f8","observation_id":"44db2749-d4db-4555-b72c-23fec72292cc","resolution":{"observed_at":"2026-08-12T11:56:37.181529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.169979Z","title":"Strategies for pre-training graph neural networks","venue":null,"work_id":"5be44a0b-0747-4c7c-8ca1-58be03eb14cf","year":2020},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.711517Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:e06a83c5ccfde4f57a991fb937f911a6f7db361c9fc7b46e3ee720ac891f2da4","observation_id":"be02c24e-0987-4b7c-9d3c-30701554b6de","resolution":{"observed_at":"2026-08-12T11:56:37.173042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-09T05:17:43.055948Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-12T11:56:36.714523Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.714523Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:ca9847e19483a8346d067c608cdf212e2bca4267a4bb3a4bec385d257e4e8c29","observation_id":"b90e845d-266e-4c63-92a6-321b51be16e0","resolution":{"observed_at":"2026-08-12T11:56:36.714523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10141","last_updated":"2020-06-17T20:30:04Z","snapshot_observed_at":"2026-07-06T09:30:11.360338Z","submitted_at":"2020-06-17T20:30:04Z","title":"Self-supervised Learning on Graphs: Deep Insights and New Direction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10141","snapshot_observed_at":"2026-08-12T11:56:36.717412Z","title":"Self-supervised learning on graphs: Deep insights and new direction","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.717412Z"},"links":{"cited_paper":"/paper/2006.10141","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:df30a70dbff4b67a482eeac2387a1d5b943e5094059354293e6619a4652e5f30","observation_id":"2b5f071a-edca-4532-b850-5188b57c75f6","resolution":{"observed_at":"2026-08-12T11:56:36.717412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05055","last_updated":"2024-02-28T01:55:09Z","snapshot_observed_at":"2026-07-06T15:14:17.404790Z","submitted_at":"2023-04-11T08:23:52Z","title":"A Comprehensive Survey on Deep Graph Representation Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.05055","snapshot_observed_at":"2026-08-12T11:56:36.721330Z","title":"A comprehensive survey on deep graph representation learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.721330Z"},"links":{"cited_paper":"/paper/2304.05055","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:b1de130759b29858aa7d033e4ed361ba43b8671db2113aa07760fe3436f29620","observation_id":"7c0bd060-46f9-4671-b455-b0c9694317f4","resolution":{"observed_at":"2026-08-12T11:56:36.721330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-07-06T05:10:16.862707Z","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":"2026-08-12T11:56:36.724786Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.724786Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:328d451d59679cd0828653da72e532133d93605cb9b81cf785e09bb2979a99ae","observation_id":"15603b7a-8909-450e-882c-a9d900eb7229","resolution":{"observed_at":"2026-08-12T11:56:36.724786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.07308","last_updated":"2016-11-21T11:37:17Z","snapshot_observed_at":"2026-08-06T06:17:37.090691Z","submitted_at":"2016-11-21T11:37:17Z","title":"Variational Graph Auto-Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.07308","snapshot_observed_at":"2026-08-12T11:56:36.728207Z","title":"Variational graph auto-encoders","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.728207Z"},"links":{"cited_paper":"/paper/1611.07308","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:133f7bf0a6eb8e1c799f58317257ca7353257490055b4b870cb0778359c774f6","observation_id":"fdd54f70-8b52-4d0f-bc8f-11ad12b0f6db","resolution":{"observed_at":"2026-08-12T11:56:36.728207Z","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":"2026-08-12T11:56:37.160876Z","title":"Prefix-tuning: Optimizing continuous prompts for generation","venue":null,"work_id":"a4f1287f-d071-4050-a427-a280d4674f83","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.731295Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:5cfdfe19664953bcb0fd039373c4a688aeb67af52bfdf9c405676d6de1f8636e","observation_id":"9c782a5e-f41b-481c-8e1c-75f0e462c076","resolution":{"observed_at":"2026-08-12T11:56:37.164467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.00190","last_updated":"2021-01-01T08:00:36Z","snapshot_observed_at":"2026-08-12T12:37:28.207761Z","submitted_at":"2021-01-01T08:00:36Z","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.00190","snapshot_observed_at":"2026-08-12T11:56:36.734339Z","title":"Prefix-tuning: Optimizing continuous prompts for generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.734339Z"},"links":{"cited_paper":"/paper/2101.00190","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:07ec4e3c7d4429c639b6c92aee3e4aff838a98b46e63c9c95b19b09114194fef","observation_id":"6504329b-965e-4061-a041-737294980414","resolution":{"observed_at":"2026-08-12T11:56:36.734339Z","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":"2026-08-12T11:56:37.150325Z","title":"One for all: Towards training one graph model for all classification tasks","venue":null,"work_id":"49f7733c-929e-4980-951b-3c3f2709342e","year":2024},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.737182Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:52ae435298a06bceaaa30bb26dc6010dcfd2c84f93f8cb40fc5d109ad447cec4","observation_id":"590b3d37-ee61-4071-8354-b22ca230d485","resolution":{"observed_at":"2026-08-12T11:56:37.153645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.138356Z","title":"Indigo: Gnn-based inductive knowledge graph completion using pair-wise encoding","venue":null,"work_id":"89789de2-d0b4-4fee-9f4c-0bb32025882d","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.739766Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:240994c4ed57b768d2a720d7ea86b6eac80e950be54be273621079c2e4cf73ca","observation_id":"34b17b8e-0c3c-46f0-8bb0-30bfd41572f7","resolution":{"observed_at":"2026-08-12T11:56:37.142235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.129666Z","title":"P -tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks","venue":null,"work_id":"c78cf090-2a34-460e-9657-b620b22d13a4","year":2022},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.742663Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:560e9139e9d78e38bff9070cfd3f5c2535c89213380303191e1557e20fd8b075","observation_id":"fb86cf37-ae06-4c05-9dc1-11c4b021d722","resolution":{"observed_at":"2026-08-12T11:56:37.132523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.120469Z","title":"P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks","venue":null,"work_id":"5490d1ec-d504-4f45-94a8-a88c80ec2803","year":2022},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.747137Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:fc96be9d7d195839c9a8e5f379ca7a4c39d220ec250ec8d4b74743ad8d65e4f8","observation_id":"be4f525c-afdc-4a59-9a88-b6988aabffba","resolution":{"observed_at":"2026-08-12T11:56:37.124007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.112077Z","title":"Pick and choose: A gnn-based imbalanced learning approach for fraud detection","venue":null,"work_id":"66e508bf-c4c6-4aff-a0ee-ff1a7eb0efec","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.749854Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:caa96db4fd8291350f120e0180885ab961dda4a097459a4670c9dd4069068e4b","observation_id":"b0da9a74-1432-4e12-ba19-057048b7a606","resolution":{"observed_at":"2026-08-12T11:56:37.115286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.103550Z","title":"Content matters: a gnn-based model combined with text semantics for social network cascade prediction","venue":null,"work_id":"4f11f68e-6c99-4f03-b3ca-fd955abe094e","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.752833Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:055cd42aa3e4ef0948b39beb02a92a6bab11637761d27ef4122a8111ecf0fd0b","observation_id":"68895dbc-ce07-4fba-94b3-d1538a9695fc","resolution":{"observed_at":"2026-08-12T11:56:37.106502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.094084Z","title":"Graphprompt: Unifying pre-training and downstream tasks for graph neural networks","venue":null,"work_id":"c1fa0aa6-25f6-4899-98eb-8358ff74a03a","year":2023},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.755456Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:26aa5bccedf04209c075e755bb41d781d0fd144d64e038daa3d458ca8d538689","observation_id":"af6c45b7-fc3f-4538-b6da-130689398c37","resolution":{"observed_at":"2026-08-12T11:56:37.097416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.085233Z","title":"Large-scale comparison of machine learning methods for drug target prediction on chembl","venue":null,"work_id":"76ea4992-6609-42f8-b94d-8c51bd9f2fc9","year":2018},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.757896Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:6577eb3e27e7e5904f7c039279c54f7154b4e93da9c8e4c5bab4bbec37b2da38","observation_id":"1a9a018d-a5fc-4520-b14f-78812456c105","resolution":{"observed_at":"2026-08-12T11:56:37.088448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.076803Z","title":"o f, G \\","venue":null,"work_id":"86b75463-ccb6-413c-9b2f-2429d5666cb9","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.760749Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:e4ddcf80a9a6d84544f2dd77a59edf868b2bf9aba7c9df8a09308d366cf0a0bd","observation_id":"09dbc28a-8002-4374-88a6-59db25a29561","resolution":{"observed_at":"2026-08-12T11:56:37.080274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.11063","last_updated":"2018-07-20T06:55:09Z","snapshot_observed_at":"2026-07-06T06:41:39.213867Z","submitted_at":"2018-05-28T17:16:20Z","title":"Theory and Experiments on Vector Quantized Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.11063","snapshot_observed_at":"2026-08-12T11:56:36.763409Z","title":"Theory and experiments on vector quantized autoencoders","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.763409Z"},"links":{"cited_paper":"/paper/1805.11063","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:879d166a2c7f701f37ddcd36fe8a563136ff6fa33317c7514a1081d6930f5b5d","observation_id":"3be2a0e9-2306-4306-b6af-200afd43eac7","resolution":{"observed_at":"2026-08-12T11:56:36.763409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07676","last_updated":"2021-01-25T10:56:45Z","snapshot_observed_at":"2026-08-07T14:12:26.620672Z","submitted_at":"2020-01-21T17:57:33Z","title":"Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.07676","snapshot_observed_at":"2026-08-12T11:56:36.766590Z","title":"Exploiting cloze questions for few shot text classification and natural language inference","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.766590Z"},"links":{"cited_paper":"/paper/2001.07676","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:0200ee81c396c62cbf31c67cbe1afddb1b3b7173d70cb035fd50ba849a02fc70","observation_id":"de07ce7c-53c2-46f2-b9c2-1ee4f7c0844a","resolution":{"observed_at":"2026-08-12T11:56:36.766590Z","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":"2026-08-12T11:56:37.067670Z","title":null,"venue":null,"work_id":"b9c297c6-359c-4efd-9312-aa54786df251","year":2015},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.769689Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:0e891abbaad7b389b46521c6cec61aa4bbafba68f453296615b2e95d68f38e0b","observation_id":"59736880-ef35-4005-9488-a261f57a32d3","resolution":{"observed_at":"2026-08-12T11:56:37.071110Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.057066Z","title":"Gppt: Graph pre-training and prompt tuning to generalize graph neural networks","venue":null,"work_id":"a985a652-5524-4227-b2f8-a3e25421cfd7","year":2022},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.772797Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:2fe628b721c521c7f4dcc33ada4a9de0878274c5ee66acbdfb210be48ad7ffb3","observation_id":"5075154f-99e7-41f4-913c-6037d1f7c693","resolution":{"observed_at":"2026-08-12T11:56:37.060568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.048569Z","title":"All in one: Multi-task prompting for graph neural networks","venue":null,"work_id":"8ab0b418-efcc-4b7a-81e4-7ede1d4b46f3","year":2023},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.775380Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:8dd9a5ac0d7df967604d3cb776a1aea468795402ccebc6174be81d186c82002d","observation_id":"17dd9e2a-6d12-4493-9dff-628dc9191072","resolution":{"observed_at":"2026-08-12T11:56:37.051928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.039271Z","title":null,"venue":null,"work_id":"2cceac5e-f8c0-4eed-8075-fd218412c806","year":2022},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.777864Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:dd956bb658e72f691fab1652c6eac108179ec5ca4b23c6103ab69ef9f6c02ec7","observation_id":"22a5dd92-4f1d-4ac6-a0d6-cb90a4ba0531","resolution":{"observed_at":"2026-08-12T11:56:37.042379Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.029979Z","title":"Visualizing data using t-sne","venue":null,"work_id":"9b0e1ae1-c516-4562-8603-d4f194d1b31c","year":2008},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.780700Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:4ae649c48f9ecbebf431726f310577d677334048a61e239603d4b99d2bc277f9","observation_id":"bb9d45bc-eb7c-43c6-8857-1194c6437c5b","resolution":{"observed_at":"2026-08-12T11:56:37.033433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-12T11:56:36.783305Z","title":"Graph attention networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.783305Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:7994b6797bf7a9f091ac124d6ea8598dad7bcfdf52aad32003bb3fed54c14fa6","observation_id":"f30edba6-d49f-44fc-bd77-54977d701d0e","resolution":{"observed_at":"2026-08-12T11:56:36.783305Z","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":"2026-08-12T11:56:37.021708Z","title":"Deep graph infomax","venue":null,"work_id":"838ca3e8-d91f-4c2f-b28f-e7ab0f14d9f5","year":2018},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.786692Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:ce502bb989c21f1322105d9f111f56e8e9b8bf33de79e1e4b53a43583f411401","observation_id":"4c872408-57fb-4dfc-8e3a-1fbfd85b8b4e","resolution":{"observed_at":"2026-08-12T11:56:37.024818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.012325Z","title":"Moleculenet: a benchmark for molecular machine learning","venue":null,"work_id":"564cb627-43f4-4891-823d-6c9b1dd697ad","year":2018},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.789389Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:7473dbe38970695aea7eab95fe846b02258ce2c11cd2630072fa9e445a82308f","observation_id":"52b32dc4-bce7-4431-a1a8-e33b7fd057db","resolution":{"observed_at":"2026-08-12T11:56:37.015514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:37.003192Z","title":"Simgrace: A simple framework for graph contrastive learning without data augmentation","venue":null,"work_id":"0c6df1f5-29e0-4102-9609-fac3be08a041","year":2022},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.792326Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:1874bc3839dce38cfdeefa77179382089fcaa9b5955516be049dbadc716d7164","observation_id":"6047e5ad-dbbe-422a-8bc4-65eeafae8a33","resolution":{"observed_at":"2026-08-12T11:56:37.006229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.795045Z","title":"How powerful are graph neural networks? In ICLR, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.795045Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:a5a388f212aa0352e61261c1bc0493f024458cd4e9d8663618503c8f574d5746","observation_id":"d79f3db1-9fda-4618-a8fb-035b6e3d5a9f","resolution":{"observed_at":"2026-08-12T11:56:36.795045Z","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":"2026-08-12T11:56:36.989729Z","title":"Revisiting semi-supervised learning with graph embeddings","venue":null,"work_id":"73ea3ed9-bf36-4ccb-ad30-a41ccfdaa7bd","year":2016},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.798346Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:4beff5688df3ed6181e905431ff63da5ea3fa66feef2ff32f76bce6635599a67","observation_id":"feb6627c-4043-4ba6-ad35-b5e308f024ac","resolution":{"observed_at":"2026-08-12T11:56:36.992724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.979775Z","title":"A comprehensive survey of graph neural networks for knowledge graphs","venue":null,"work_id":"128e7047-6ae2-4d9e-880b-8ac21854ba9e","year":2022},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.800953Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:f2f7f005b139d9e8200f6c39b0930436f642d0605ac12ae0d031bcb5b38b05d2","observation_id":"10b1a074-a73c-423f-a15a-544314292044","resolution":{"observed_at":"2026-08-12T11:56:36.983037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.969774Z","title":"Graph contrastive learning with augmentations","venue":null,"work_id":"b9001f8b-2a7b-4292-ad64-133d231eadc3","year":2020},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.803691Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:a0cde28cda8afba1c22232a4ea5a3625bdd37822719b1c9b23ae709d7a77a171","observation_id":"4b7a9462-60cf-4df7-af72-9e14fc7ea0db","resolution":{"observed_at":"2026-08-12T11:56:36.972963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.959995Z","title":"Graph contrastive learning with augmentations","venue":null,"work_id":"452bc702-56ab-4cc8-82b9-cde1f403a5e3","year":2020},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.806203Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:75a4662f4a4b1c5d7c755feb756d97c48c79cf51445c6988a3c0533b596e3c16","observation_id":"70441fcc-c42b-4114-aeeb-dc069f77f506","resolution":{"observed_at":"2026-08-12T11:56:36.963301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.952152Z","title":"Graph transformer networks","venue":null,"work_id":"c62da280-8d7e-45ad-adc8-08592802f954","year":2019},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.809161Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:09c16709447fb1ed7fbfd44d86696fae0cceb50844c1ded5573fb722b9b4bf45","observation_id":"78a7ef28-4cd6-4996-9ac4-d83a02a7e1cc","resolution":{"observed_at":"2026-08-12T11:56:36.954851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.943625Z","title":"Graph transformer networks","venue":null,"work_id":"df893682-997c-4952-b233-be6394d75c56","year":2019},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.812002Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:091373986805630c3b22e5acfcdb50983756b693adc1083a15fb6055acc1cae9","observation_id":"46078f27-f64d-4811-9081-79f4d853aeaf","resolution":{"observed_at":"2026-08-12T11:56:36.946571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.934348Z","title":"Beyond fully-connected layers with quaternions: Parameterization of hypercomplex multiplications with 1/n parameters","venue":null,"work_id":"74b7b51b-d2d7-47f1-828d-9537463b8566","year":2020},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.814762Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:65b2b5ef478bc31094ad5d2d81eaa457affa04ff28254bbfc5377f9712bdcdb4","observation_id":"b5e5c53c-d51a-4b57-9570-b72ad7a24cad","resolution":{"observed_at":"2026-08-12T11:56:36.937831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.922539Z","title":"Graph contrastive learning with adaptive augmentation","venue":null,"work_id":"b975a58b-8798-4a0b-b1ce-eabbf3f44a59","year":2021},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.817364Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:107c2d5e6dc56620575f3716c39606389be1f2569259fa4237cc555ea21e1ae9","observation_id":"9687f69f-9957-4587-8b0b-0a6624cb214a","resolution":{"observed_at":"2026-08-12T11:56:36.928291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T11:56:36.819840Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.819840Z"},"links":{"citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:ee4ca6afcff9bbed37c4a446cfa98c80b1032c6f3965ba3607bd3e93acc3616e","observation_id":"504c79af-3172-412e-bedc-32fc38f75566","resolution":{"observed_at":"2026-08-12T11:56:36.819840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T11:48:49.789243Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":39},"total_outbound_references":54},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2411.17676."}