{"as_of":"2026-08-11T01:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:daed9305d934aebb13f1af097ebb4ee8f12b3fbf1014de246f6d65fb53b95d0f","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:55:56.571370Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.16903/citation-record","integrity":"/paper/2505.16903/integrity","json":"/paper/2505.16903/citation-record.json","paper":"/paper/2505.16903"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:56:06.984403Z","title":"Learning with pseudo-ensembles","venue":null,"work_id":"70dd3b33-d308-494c-94d6-adb556af99d4","year":2014},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:49.618688Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:e4dfecfce92cd623c3a13652cdd2482f0ccfb85231c7fd384a8bb0a414574209","observation_id":"d257c873-1b4c-4f2e-9048-17e05a58475a","resolution":{"observed_at":"2026-08-07T14:56:07.041166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:06.887735Z","title":"Evaluating robustness and uncertainty of graph models under structural dis- tributional shifts","venue":null,"work_id":"eaba4d8e-cb94-4b14-8438-63fdca4399b3","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:49.683773Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:378b420e31d93d38294c7d212857833bcacde7ac37c90d7226de86476662e5f6","observation_id":"9b134781-6ca8-4bc0-9a81-bec4684763ad","resolution":{"observed_at":"2026-08-07T14:56:06.935023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:06.788643Z","title":"Borgwardt, Cheng Soon Ong, Stefan Schönauer, S","venue":null,"work_id":"733de11a-cc90-42b2-aeaa-67b4c16770bf","year":2005},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:49.742644Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:711b344510fda6dc583baab7f389aacf0f9e5dd0348885e4c71d22f41ac5ff1a","observation_id":"f1d0bd4b-9a1f-44b8-93f4-e15f79665d20","resolution":{"observed_at":"2026-08-07T14:56:06.860026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:06.616382Z","title":"Introduction to Statistical Learning Theory, pages 169–207","venue":null,"work_id":"8b7f50e2-23f9-4c93-8e2f-cd13a4b4f7c7","year":2004},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:49.807485Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:cf559d0eb92e659aa0bed408104374504a38e299c71a2714a270f71465c8d92f","observation_id":"aa3b10c1-9afc-456e-9ec7-a3d88885d43a","resolution":{"observed_at":"2026-08-07T14:56:06.701177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:06.418725Z","title":"Language models are few-shot learners","venue":null,"work_id":"34aa69ba-ea0c-409b-ab8d-53218455e4e0","year":1901},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:49.870199Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:078d818c11825ad3b33dc60de88b6e5e3532615cad68ff7c4b343ec72331ad1e","observation_id":"afce7704-ff9c-4eee-87ea-bbbe5c910549","resolution":{"observed_at":"2026-08-07T14:56:06.499957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:06.298066Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":"d65a5a40-cd85-4a8b-9578-c2a5ac04d6ec","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:49.919894Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:ab7e390e0ef4bc6962ae838900638617a264c7756bafdf1f9f332b3df8fd41a3","observation_id":"986d933b-baff-4e2a-9bb1-f210cf8054b2","resolution":{"observed_at":"2026-08-07T14:56:06.354919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:49.967186Z","title":"BERT: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:49.967186Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:3385a469277b345aa30d77228d2855363a4214a3373a0a8eae599b51d97204af","observation_id":"6c89f709-a6f5-4b74-92d6-b921ea1064fa","resolution":{"observed_at":"2026-08-07T14:55:49.967186Z","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":"2026-08-07T14:56:06.031522Z","title":"A closer look at distribution shifts and out-of-distribution generalization on graphs","venue":null,"work_id":"1a54f495-5d16-4db6-b592-e3db9f310f5d","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.041199Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:819a1d479f6ba92c05b10f6f0ac56fdd7e13a0ffe401ab12948c1edd4c586f30","observation_id":"11a3bd6c-e4ca-4509-9c1a-4ade47053e4d","resolution":{"observed_at":"2026-08-07T14:56:06.158174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:05.880378Z","title":"Generalizing graph neural networks on out-of-distribution graphs","venue":null,"work_id":"78d76564-129c-47e3-8cdf-2101a3e839eb","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.131100Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:e9a96713970107f117cb613191c2538bffc45f72215fe4a521dffd7488d59eba","observation_id":"fcd83865-fa84-4269-a48a-644cc722c5e5","resolution":{"observed_at":"2026-08-07T14:56:05.965968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:05.624387Z","title":"Universal prompt tuning for graph neural networks","venue":null,"work_id":"7d6b4fb1-8cf7-4f1e-99d7-2547632ff392","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.195608Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:bbe7bd49f896fad1ea50734032ad3426921ffe7567d0dd3eef26c33f7ac13b30","observation_id":"c3ff7dac-24e4-4bec-8c00-f6f8cb969f02","resolution":{"observed_at":"2026-08-07T14:56:05.748542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:05.402038Z","title":null,"venue":null,"work_id":"11cb7648-9c0c-4145-a939-da8d2685f750","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.261589Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:231d93cbe3dc38749118f13d90d81679ec7b9cf1111de28c70d13677983228a1","observation_id":"f81977ea-3728-4b3c-8a8e-823f967a4972","resolution":{"observed_at":"2026-08-07T14:56:05.510968Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:05.222802Z","title":"Talk like a graph: Encoding graphs for large language models","venue":null,"work_id":"a9320790-210f-4976-91ab-cf3f98a9778e","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.328993Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:18d68340dcea48f3467f965860836a4c8dd3d433f5d6b48df14c2c98b765a7de","observation_id":"6cbf7e44-dd1f-4cfa-8d5a-16b1e58978a7","resolution":{"observed_at":"2026-08-07T14:56:05.299011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:50.402908Z","title":"Schoenholz, Patrick F","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.402908Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:6ea9ef61d4ed13849f16cafd21a995760d97f0bfcfd39123384c05be94854e8f","observation_id":"20ad9a37-55f2-49e8-8a2d-f406a7a7ccdb","resolution":{"observed_at":"2026-08-07T14:55:50.402908Z","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":"2026-08-07T14:56:05.015537Z","title":"GOOD: A graph out-of-distribution benchmark","venue":null,"work_id":"612bf4d1-c65b-41f8-83e5-b8b10742e0a0","year":2022},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.454808Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:023e629a0de489108ee5aa70d9d04d5770eb0401c9c1ba522de40dc018598dc9","observation_id":"225e9f8f-8993-4308-a832-a0355cb4bfa2","resolution":{"observed_at":"2026-08-07T14:56:05.137504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:04.806196Z","title":"Parameter-efficient fine- tuning for large models: A comprehensive survey","venue":null,"work_id":"efc14e4c-5a1d-4fc0-ba4d-9cd80d698749","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.520930Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:3d58947b2f5fe59ca8414fdeff37fee2b11fa82f301b19c330f967c31466cbcd","observation_id":"2047e077-72b2-469b-8740-427d8a4ea753","resolution":{"observed_at":"2026-08-07T14:56:04.885892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:04.524205Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":"5e31b9ab-79b3-49e8-86c0-dce65785cb86","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.581766Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:06cba1854a71f10ee801f932f87babc65805283d90f2cffa9ff6ae5a9c6ccbfc","observation_id":"df9ab7ed-85d6-43fa-9906-58b69214ab20","resolution":{"observed_at":"2026-08-07T14:56:04.701038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:04.293183Z","title":"Learning discrete representations via information maximizing self-augmented training","venue":null,"work_id":"ed4c8cd9-277e-49d5-867b-b0dd465ddb3c","year":2017},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.647381Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:d6fa40238a41f7534dc165cfc5dd7f1d75b9acc20197f2e4e834fc2f868f0d28","observation_id":"454cbce8-e36f-493a-a107-8a5663a61aba","resolution":{"observed_at":"2026-08-07T14:56:04.400545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12600","last_updated":"2023-05-21T23:16:30Z","snapshot_observed_at":"2026-07-06T15:30:22.612066Z","submitted_at":"2023-05-21T23:16:30Z","title":"PRODIGY: Enabling In-context Learning Over Graphs","version":1},"cited_work":{"arxiv_id":"2305.12600","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.12600","snapshot_observed_at":"2026-08-07T14:55:56.668414Z","title":"PRODIGY: Enabling In-context Learning Over Graphs","venue":"cs.LG","work_id":"ab0b5314-7781-4733-becb-e82601cf2e92","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.719538Z"},"links":{"cited_paper":"/paper/2305.12600","citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:462e1eb49fbaa3368740eda80d21dd47022e2450036199e0ceab67c3e61c8ec5","observation_id":"ed58f630-4fee-4ee1-a178-54d07b62d0f9","resolution":{"observed_at":"2026-08-07T14:55:56.716185Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:04.112128Z","title":"Domain adaptation without source data.IEEE Transactions on Artificial Intelligence, 2:508–518, 2021","venue":null,"work_id":"8f175b2a-2fd9-4d2c-a0f9-1171bdad6b2a","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.787013Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:de35ae4e4ddbace068b069abdfbea96a679bef28b0dbe5726b25a477d8c6e8a8","observation_id":"0092571c-34cd-4bfc-b962-4f2576c112dc","resolution":{"observed_at":"2026-08-07T14:56:04.185665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:50.867915Z","title":"Kipf and Max Welling","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.867915Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:3138088030b232a5ba8413e795c28c415b9399cd8a7b1914bb3eb627bd294e86","observation_id":"cfd877be-fccd-4188-a58d-4f0dc757c722","resolution":{"observed_at":"2026-08-07T14:55:50.867915Z","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":"2026-08-07T14:56:04.094287Z","title":"Understanding attention and gener- alization in graph neural networks","venue":null,"work_id":"6fac2df3-95d6-4052-b1c8-dfbc606b0e19","year":2019},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:50.935811Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:dab3e8350ea0f8b1934742e82630b939ee4bd7fec8d7cc972c37f2a1ef5bef91","observation_id":"e6483b66-ddeb-4adb-a9fa-f391c4eef021","resolution":{"observed_at":"2026-08-07T14:56:04.097385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:51.010813Z","title":"Large language models are zero-shot reasoners","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.010813Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:04eec8b01b9debb5f15aff3afaaa10f7b7b2fabd7291f3496895fd27b8bdb0bb","observation_id":"37bf2992-3cfa-40f4-b295-4b7666442af3","resolution":{"observed_at":"2026-08-07T14:55:51.010813Z","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":"2026-08-07T14:56:03.923619Z","title":"Temporal ensembling for semi-supervised learning","venue":null,"work_id":"c24a4c74-8d2b-464b-b7c5-a9c4c0945c77","year":2017},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.095777Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:15eea49017e6a7c416c92d632b918f268eebfd356ef6651201a5ad7e832a8c5f","observation_id":"5997c0c8-87c4-4c02-9b8d-609be65ebb72","resolution":{"observed_at":"2026-08-07T14:56:04.002691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:51.098895Z","title":"Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.098895Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:252d4ebe4eef290e887058bb4bc363ee3d0eae59dd2e97be37e685a6b46220f0","observation_id":"4c6eda5d-8e44-4e08-b385-dc2551657632","resolution":{"observed_at":"2026-08-07T14:55:51.098895Z","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":"2026-08-07T14:56:03.816973Z","title":"The power of scale for parameter-efficient prompt tuning","venue":null,"work_id":"06e40746-0a30-4b1a-a0cd-e507b9bbfc5f","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.101539Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:6066faae88d34b101c00c8073271c10f1f325175fe7ae6fc2cd32746f190be76","observation_id":"bde2553f-798a-4a1e-9e1c-a66115454b70","resolution":{"observed_at":"2026-08-07T14:56:03.861324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:03.672267Z","title":"Ood-gnn: Out-of-distribution gen- eralized graph neural network","venue":null,"work_id":"36caa698-79e4-4194-8e91-78dfd78786a2","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.116145Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:f49fe8644d1d3372920509096490e92e25a05fec5cc4a4ca49b3d1035db3d851","observation_id":"c85fb3db-9cf3-4fbf-8362-082a9303db6a","resolution":{"observed_at":"2026-08-07T14:56:03.727622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:03.539119Z","title":"Learning invariant graph representations for out-of-distribution generalization","venue":null,"work_id":"3bec7fe6-b561-415d-a378-32054fe7ce04","year":2022},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.170374Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:e73feea5ebeee6f4917c2502c9f2986903e1553e5ddacb1169ddb4c5ea8abece","observation_id":"624731d0-6c28-4402-bd52-b1f091a7e7ea","resolution":{"observed_at":"2026-08-07T14:56:03.608611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:03.385376Z","title":"A comprehensive survey on source-free domain adaptation","venue":null,"work_id":"25dfa71e-b1e6-46f5-8981-2a1582a5ae05","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.249661Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:3113fcd9f1745365d00cd3d53ce587885912eca24f753fab1845ab4b1c698c84","observation_id":"f696fdaa-fd53-4fc1-b570-f31f322b1c26","resolution":{"observed_at":"2026-08-07T14:56:03.452106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:03.261442Z","title":"Prefix-tuning: Optimizing continuous prompts for generation","venue":null,"work_id":"2793468b-e7d0-4b98-a632-e7a0abca84ad","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.362305Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:188992be7ac95b9adc9d327dc6aeeab73d37ac22fb02cca8ecbff036002f7dcf","observation_id":"6e86500c-95bf-4d58-8f09-36269a30a27a","resolution":{"observed_at":"2026-08-07T14:56:03.303455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:03.161716Z","title":"Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation","venue":null,"work_id":"4d6ffdb0-eeac-4caa-878c-119a166506a1","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.445050Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:91f4fc672a08066fc4cd9105fba8391832f2f7f83b962fb2bc1778f5f77ac0f5","observation_id":"4e8cadf3-f49d-421e-8230-6bd1bcdf06cb","resolution":{"observed_at":"2026-08-07T14:56:03.225424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:51.593423Z","title":"Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.593423Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:3a81c3b10af867a8d36b5ef807ffb91361acebbda825a4e642db5a3930b66cb4","observation_id":"55916dc3-0970-402e-9a0d-3471c48429be","resolution":{"observed_at":"2026-08-07T14:55:51.593423Z","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":"2026-08-07T14:56:03.012351Z","title":"Large scale learning on non-homophilous graphs: New bench- marks and strong simple methods","venue":null,"work_id":"a98f58ef-520d-450e-b6eb-d31cb547f248","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.695948Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:5fc5f3e7000a3ebe2793a68ebf53d1291880e9423cb27edf728c22d71855ca6c","observation_id":"c9e97234-dd54-40c8-a6d7-17974f6273cc","resolution":{"observed_at":"2026-08-07T14:56:03.063031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:02.908607Z","title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","venue":null,"work_id":"eef249b4-c070-49e7-90d8-d3456a04da0d","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.813761Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:ccf6cd533b906e0f4cca3c1e9c70172f0896174b8d901b8d84bef19da41874d4","observation_id":"54a055c7-6201-4abd-8fca-bb8f22231dea","resolution":{"observed_at":"2026-08-07T14:56:02.947953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:02.753802Z","title":"Graphprompt: Unifying pre-training and downstream tasks for graph neural networks","venue":null,"work_id":"22ad8d26-2f6d-49ad-943b-316bb261254a","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.906821Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:fa10bdb9dec5fcb1a058b072780e0fa121f39f1ff55853967b3076d7714e4bb9","observation_id":"64205ed2-8aaa-4d68-ab23-6ddf2e323b79","resolution":{"observed_at":"2026-08-07T14:56:02.806422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:02.647319Z","title":"Position: Graph foundation models are already here, 2024","venue":null,"work_id":"a2cf1097-70c0-4c2d-926e-7e05ea299e89","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:51.980886Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:e1891ae633eebe0e561d85828c78360fc4bf483a4051850cf26338df1623f8df","observation_id":"dff5aec3-812c-4ba0-8648-17f358b48e5a","resolution":{"observed_at":"2026-08-07T14:56:02.682231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:02.447299Z","title":"Refram- ing instructional prompts to GPTk’s language","venue":null,"work_id":"cce40bea-daa3-432b-b15e-07c54a050463","year":2022},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:52.100908Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:7643d1378373cafd1c77adb8f17937d9931e23850de7258e07d15c14defbe172","observation_id":"ea2b5711-d8c8-4e40-b361-d50a8117d62c","resolution":{"observed_at":"2026-08-07T14:56:02.531300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:02.354028Z","title":"Future directions in the theory of graph machine learning, 2024","venue":null,"work_id":"ec83a932-30e0-4cb0-8e40-a7323af92e9d","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:52.267083Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:4332f7061f56f4fa8c8c090f7606ba5ceca398018067dc77bc96f5fc762b70d6","observation_id":"a2229adc-782b-492c-b5c8-466db9ffb899","resolution":{"observed_at":"2026-08-07T14:56:02.389449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:52.405753Z","title":"Automatic differentiation in pytorch","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:52.405753Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:65732416b41cb1680c6a836877efd872136112c7d062366e9266b9eeadba8f7c","observation_id":"4b6c2910-d4c3-4d9d-aadf-d0e5453727b0","resolution":{"observed_at":"2026-08-07T14:55:52.405753Z","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":"2026-08-07T14:56:02.247193Z","title":"Geom-gcn: Geo- metric graph convolutional networks","venue":null,"work_id":"69acf3ea-c025-42b3-b5c8-0e9dd2db4f91","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:52.537474Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:394e9be69d88209ad9b85e7531731f41ab3da445fd3acae1adc6921635aca2e8","observation_id":"fe3c9be3-2b77-4cb3-b9f5-fdabf093b7bb","resolution":{"observed_at":"2026-08-07T14:56:02.300413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:52.629180Z","title":"Improving language understanding by generative pre-training","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:52.629180Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:a6ea43423e6ead6c2eece7cc568d4c811908dacc7b19c145d7731f13438f9485","observation_id":"183f8cb9-7c0b-433b-bd85-d6fc8576abb4","resolution":{"observed_at":"2026-08-07T14:55:52.629180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:55:52.761380Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:52.761380Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:b3108bfdcf3e0ca1d02c85a5673b6f9f4cf36414cc90af42fa437cffc6d0b19d","observation_id":"0500c3be-bd1d-4525-8e36-06677cbd2a88","resolution":{"observed_at":"2026-08-07T14:55:52.761380Z","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":"2026-08-07T14:56:02.089019Z","title":"Peters, Swabha Swayamdipta, and Thomas Wolf","venue":null,"work_id":"cac9aa98-af92-4f0a-9250-deb264f0b59c","year":2019},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:52.914811Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:09b943872a3acfe35b3589891ae314b1cb787bcf6ed6bc1470c09fd553e5b2e6","observation_id":"7dc8cc61-7a83-49b6-95d3-f980804493cb","resolution":{"observed_at":"2026-08-07T14:56:02.165809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:01.954966Z","title":"Regularization with stochastic trans- formations and perturbations for deep semi-supervised learning","venue":null,"work_id":"621787ae-73d2-4388-bc62-f8618c4cdc18","year":2016},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:53.066300Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:394b2f589555899c83b1ec04e52fd1f01307f8248b2c84ec5102c90a24a07343","observation_id":"b535f4f7-e887-46e2-8a96-0c4258765ce4","resolution":{"observed_at":"2026-08-07T14:56:02.026777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:01.819548Z","title":"Brenda, the enzyme database: updates and major new developments","venue":null,"work_id":"540277a6-174e-4324-b2f2-def526538c0f","year":2004},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:53.210104Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:7d57c8cce78099116d22b73c4d0d372a482a7b5c652c0f72c0a32b1a727961c6","observation_id":"8f98ec28-ec12-4b7c-bf0f-07617bc5acc7","resolution":{"observed_at":"2026-08-07T14:56:01.869274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:53.356290Z","title":"Understanding Machine Learning: From Theory to Algorithms","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:53.356290Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:0f4be8039782f23e025a3117dbe546bff6b781bd6a06514c7931137a33379e9b","observation_id":"98a5b8ec-535a-4680-acb8-6fe826bdb4c0","resolution":{"observed_at":"2026-08-07T14:55:53.356290Z","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":"2026-08-07T14:56:01.598165Z","title":"Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel","venue":null,"work_id":"41295e41-9abf-4351-9fad-9517592ed3ce","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:53.506298Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:a9666193affc2617b85a6ebca69fa097f9ee2e0ef0ba5ff2e20b57950b8860e4","observation_id":"d4ec7e23-e3c5-4fe1-bc4e-3d91bd8700a4","resolution":{"observed_at":"2026-08-07T14:56:01.686563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:01.413920Z","title":"Unleashing the power of graph data augmentation on covariate distribution shift","venue":null,"work_id":"898dc4b1-2d93-485a-8837-071adc1f406f","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:53.630491Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:936227de8de25d07a92906523068eda3b95ebf20493269afb4d846eb5b6a233c","observation_id":"f0136936-e334-4c38-a50b-93746bfaf848","resolution":{"observed_at":"2026-08-07T14:56:01.500218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:01.298617Z","title":"Gppt: Graph pre-training and prompt tuning to generalize graph neural networks","venue":null,"work_id":"1a69dc4d-1c48-4813-93c5-9bed54913fb4","year":2022},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:53.758619Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:87c08d0a8575ff0c092682c291c9a290b73ac91a730250874a406885ec13f167","observation_id":"5d4b3621-9755-4ded-9fe0-7361d23f5941","resolution":{"observed_at":"2026-08-07T14:56:01.333906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:01.096039Z","title":"All in one: Multi-task prompting for graph neural networks","venue":null,"work_id":"d3c15f85-dfe7-4228-9c5d-ed15bd2cf023","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:53.906374Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:1396dbb1f138ba58ff2b554ff6448864562ff96279dfc92537ad205d29c7b0e5","observation_id":"052d1d37-7c2a-4c45-9bb1-eff5a852e665","resolution":{"observed_at":"2026-08-07T14:56:01.232875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:00.970902Z","title":"Sutherland, Lee A","venue":null,"work_id":"71f5317c-6243-427a-9583-e6ea3cab3f97","year":1906},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.069606Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:bba7c5b366a9c87c67b9a5e52f8b63219f8f559a84f564702d5488d7a7d8a217","observation_id":"c56a0294-d8c7-4635-a253-f3eb30352a2c","resolution":{"observed_at":"2026-08-07T14:56:01.049458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-07T14:55:54.218271Z","title":"Representation learning with contrastive predictive coding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.218271Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:b992b8b40160a88061e81a485e54a021901aaaccca5d3c7e658eb8fd8dc7cd1a","observation_id":"4b444c0b-7a9b-4583-9898-924c37563d0b","resolution":{"observed_at":"2026-08-07T14:55:54.218271Z","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":"2026-08-07T14:56:00.760977Z","title":"Graph attention networks","venue":null,"work_id":"a9c63a22-15d8-40c5-8384-eace5f1e65a7","year":2018},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.369902Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:02de7b01cfd68d2eccbe4ba594736c221e13e277ec6a2f356ed7e0223a1d390a","observation_id":"2526aa56-9e31-4100-9cad-6bbf6d3c857c","resolution":{"observed_at":"2026-08-07T14:56:00.872036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:00.610570Z","title":"Freematch: Self- adaptive thresholding for semi-supervised learning","venue":null,"work_id":"ccde668f-95b0-44d3-b130-0113d4d57178","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.458918Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:f2d2c9483ae3512c9cecfdf694110bc96d489aefe3eddfd2ad81e4b1babbcd54","observation_id":"8b3115db-2567-4246-877b-94d5ba4086ea","resolution":{"observed_at":"2026-08-07T14:56:00.688036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:54.532975Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.532975Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:e215c5caa9302806c90904ceaac5b05ad0e70039eba861710667478540f28678","observation_id":"b845fb66-32e7-4217-b75d-445875b1139f","resolution":{"observed_at":"2026-08-07T14:55:54.532975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:55:54.601643Z","title":"Discovering invariant rationales for graph neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.601643Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:c1d4d5cc306a60d5bacd76029e18111aee103216b7ed7ce9fdb78321627b9173","observation_id":"1046677f-98c7-4c40-870f-27f49d6616b5","resolution":{"observed_at":"2026-08-07T14:55:54.601643Z","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":"2026-08-07T14:56:00.453413Z","title":"Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S","venue":null,"work_id":"2590b999-99a4-4b7f-871a-7a5d5d9e00ea","year":2018},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.696560Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:e74dee5fa37e0f88adaef28be548a8d800e490b3916ec43207961315743c5195","observation_id":"0b7c1538-35c3-43e6-b487-cc7fb766c850","resolution":{"observed_at":"2026-08-07T14:56:00.518218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:00.328486Z","title":null,"venue":null,"work_id":"6aa59d5f-a942-4d0b-b0f2-204494d376e9","year":2022},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.785787Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:86067addbb0a04a2cfceebe455d42f18ca010aca2f338428c8702cb99e9685c7","observation_id":"d26e695e-ccd9-4a6d-afeb-6159572a3f4a","resolution":{"observed_at":"2026-08-07T14:56:00.382223Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:56:00.126559Z","title":"Hovy, Minh-Thang Luong, and Quoc V","venue":null,"work_id":"a119e4cb-8ff0-43c1-8b11-d5729a4fce9b","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.875443Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:deefabf15b044d4a80507e0a71114e58d2c8804a3cc7e73b944ca98d5d67aa2e","observation_id":"7829155a-bf19-437b-904d-cd8140d0b221","resolution":{"observed_at":"2026-08-07T14:56:00.205463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:59.960092Z","title":"Large language models as optimizers","venue":null,"work_id":"9bc52911-4af9-4e2e-801d-45e59fd4c2b1","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:54.946532Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:548722f389c26d7260115d0fd56e0aae4e96da08b9377bda46b4c84dbae35dc9","observation_id":"11f28749-24d3-48a6-aac9-7c1bd309db72","resolution":{"observed_at":"2026-08-07T14:56:00.024024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:59.808645Z","title":"Generalized out-of-distribution detection: A survey","venue":null,"work_id":"2f696523-88c1-409b-8aec-8200b0618042","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.017219Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:9bb1177dacb0075efab04cb00faf213c4e009dc227379bbe6581efb305cfe173","observation_id":"58fc1fa9-227d-4598-8d1e-0cb9c503d976","resolution":{"observed_at":"2026-08-07T14:55:59.886384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:59.659902Z","title":"Generalized source-free domain adaptation","venue":null,"work_id":"935433a9-d1e2-4b2b-8598-211b2bd66ae4","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.096786Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:634a42b47650d4cc4e936c17acb10c636c79ab707581b50006db412e6d722954","observation_id":"33aefa72-7a34-4fb8-b46c-199a7be2cd2b","resolution":{"observed_at":"2026-08-07T14:55:59.711604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:59.523809Z","title":"Cohen, and Ruslan Salakhutdinov","venue":null,"work_id":"93040654-4720-4f34-a192-173bda945021","year":2016},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.159829Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:c5f234e29f8bebdec0844ad2fed62a2d0b1f255850092f8cb8fea1117302a392","observation_id":"20510fb2-0a90-4be7-9f6c-329dfc002dc4","resolution":{"observed_at":"2026-08-07T14:55:59.575414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:59.367632Z","title":"Gnnex- plainer: Generating explanations for graph neural networks","venue":null,"work_id":"7106784c-3aa1-49af-a652-270cba3a46db","year":2019},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.245702Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:8a5c6ad7100acceefa70fd3daf8d78259f050e3713165dfc2b1b0d405b32974b","observation_id":"8e375dd7-4092-481f-ad20-f24bf000ce78","resolution":{"observed_at":"2026-08-07T14:55:59.440074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:59.182037Z","title":null,"venue":null,"work_id":"b4e6ca59-5e50-42a6-a5a8-bfce793b1449","year":2019},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.336838Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:cbbccc0907e0b287e3fa5e0d2130507979a4d79762fa40d60b644b9d1c1ee720","observation_id":"e715c3a2-2566-4f6d-996b-9457fc467f31","resolution":{"observed_at":"2026-08-07T14:55:59.289550Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:59.041918Z","title":"Graph contrastive learning with augmentations","venue":null,"work_id":"ed319860-abd1-40d3-8715-960ea7e187d9","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.432385Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:ea9e983fceee9bfd2798337442cb41aad7a346efddc261a33db0ef96c16d7e1e","observation_id":"d057e598-97ef-42d1-b2b5-b0925cee37f9","resolution":{"observed_at":"2026-08-07T14:55:59.081472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:58.853590Z","title":"Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs","venue":null,"work_id":"a58e0ebf-d0f6-40c0-b5e6-0a597ae85cb1","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.510798Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:972a9c8203aa5287549700f93cdd52cf76600c5ebee3cb5d94ea1d3365c854e0","observation_id":"941c8298-f3ae-4a66-aa23-c47b5744cee5","resolution":{"observed_at":"2026-08-07T14:55:58.930446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:58.641415Z","title":"Node-time conditional prompt learning in dynamic graphs","venue":null,"work_id":"8d436098-30be-4956-b93c-216719d53a75","year":2025},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.570037Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:ef784039077b089248f7ef6191c5f80f149c21d96c739c8d86ed5c3b082a5a57","observation_id":"738dbcd0-4420-4ee6-8908-42569993f595","resolution":{"observed_at":"2026-08-07T14:55:58.729476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:58.470637Z","title":"Multigprompt for multi-task pre-training and prompting on graphs","venue":null,"work_id":"d97e0fa6-dbeb-47e6-8526-9a2340787500","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.641166Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:068d72d27d231d43f6e48226b85a5709de8b46f582691a98015bad17149ab152","observation_id":"6d1895a2-a9cc-412c-9b68-9f3d0c069f67","resolution":{"observed_at":"2026-08-07T14:55:58.542888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:58.296349Z","title":null,"venue":null,"work_id":"670083ab-47f9-4f23-8b48-345054326913","year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.699642Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:08e53e56c2a73ea4eaabac88885321c96d45ee7cde6c1f36427a7fe1a11d49a8","observation_id":"e073f812-dc8a-420c-9c12-95d23532c7bd","resolution":{"observed_at":"2026-08-07T14:55:58.393038Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:58.085883Z","title":"Graphsaint: Graph sampling based inductive learning method","venue":null,"work_id":"4ce55b49-1b7b-4e66-a459-96448d18d935","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.808628Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:3bfa7fc26383b51c91ec4b91adb1fb9149c2d2a156b5470c82b53ffa1dda7368","observation_id":"10f9203d-e583-42c2-a4e9-c51db517f69a","resolution":{"observed_at":"2026-08-07T14:55:58.222822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:57.854628Z","title":"Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling","venue":null,"work_id":"5e14a4b2-a3bb-44bb-ad3c-5c7a08a77991","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:55.902502Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:979dca0e7d18ca199cb20cb2949d671d262b532ed04335d9fb9d2cad51117b9d","observation_id":"be889941-f9c7-4a76-aeb0-f26efb2df9ca","resolution":{"observed_at":"2026-08-07T14:55:57.993901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:56.001394Z","title":"Large language models are human-level prompt engineers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:56.001394Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:e0fb38970157f81ad5c168084f376d30398461c34f9d71d77ca9e270e53f61cb","observation_id":"bd746849-2516-4f71-bdfd-c9a09894dbd0","resolution":{"observed_at":"2026-08-07T14:55:56.001394Z","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":"2026-08-07T14:55:57.617770Z","title":"Beyond homophily in graph neural networks: Current limitations and effective designs","venue":null,"work_id":"e3859b3d-0a54-4771-aa9d-2edc07d2f8c3","year":2020},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:56.115090Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:735c6220195c7df665110ba74a107d1ad03c1eb566d30b1c16d1a64d6b3a5227","observation_id":"6b9dbee7-2732-44e6-970a-f647c1f0b7cd","resolution":{"observed_at":"2026-08-07T14:55:57.727154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:57.436178Z","title":"Shift-robust gnns: Overcoming the limitations of localized graph training data","venue":null,"work_id":"3ce51560-315f-4306-b887-fd05899d4ad9","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:56.228567Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:e18fd78ce463324f92f86adbf5dadf3e7e05e2a0dad3e936142d4701f53aa4d6","observation_id":"87a27c74-d7eb-4077-8836-af34d54d17c3","resolution":{"observed_at":"2026-08-07T14:55:57.533331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:57.197045Z","title":"A comprehensive survey on transfer learning","venue":null,"work_id":"9c9c5228-63af-48c4-b780-a3372c4097de","year":2021},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:56.378199Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:56f85b302722763a6deb206b78b05ae81864950c341482e7985a31173225d6c8","observation_id":"4981515e-6510-4d25-8ee1-0dab563e0d45","resolution":{"observed_at":"2026-08-07T14:55:57.309058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:56.970766Z","title":"Prog: A graph prompt learning benchmark","venue":null,"work_id":"3d501f90-0bb5-4031-9c74-08130cd5f848","year":2024},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:56.439234Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:b34b6a60cdfb4a676010d37f8ea1c2502482dda8cfe1f97edc262a8eacc02257","observation_id":"e48d6489-4796-4204-90d5-1b1fc6f768be","resolution":{"observed_at":"2026-08-07T14:55:57.100564Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:55:56.775982Z","title":null,"venue":null,"work_id":"3af142df-ff62-42ab-b60c-4d692a215148","year":null},"citing_paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T14:55:56.571370Z"},"links":{"citing_paper":"/paper/2505.16903"},"observation_digest":"sha256:f3c7c79416befb294effae1b3c984231204076ec6c3c6b0e6fd0c0b89bdd75ca","observation_id":"38e82049-c63e-4731-a860-6de3ed5f5c75","resolution":{"observed_at":"2026-08-07T14:55:56.857305Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.16903","last_updated":"2026-06-29T01:12:34Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T22:51:11.506047Z","submitted_at":"2025-05-22T17:03:20Z","title":"Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":1,"verified_fuzzy":56},"total_outbound_references":77},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2505.16903."}