{"as_of":"2026-08-19T01:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1d9021c6f53e82c54eeef029d390de15bea24b589b5b4d0b3ee8dccdabbb8adb","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T14:40:13.270083Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2501.15142/citation-record","integrity":"/paper/2501.15142/integrity","json":"/paper/2501.15142/citation-record.json","paper":"/paper/2501.15142"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:40:13.818498Z","title":null,"venue":null,"work_id":"65c66ad7-fd2d-4052-80e4-795745082401","year":2021},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.089187Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:dd63ba8cc65236e822689c0cf546e1664c5946dad6a86fd627919ed6199d69c5","observation_id":"eb9e4a31-31b7-4978-a474-aac4f1f1241a","resolution":{"observed_at":"2026-08-10T14:40:13.822553Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.805480Z","title":null,"venue":null,"work_id":"afe40329-dd7a-4e03-859f-a59e9e0ee4e1","year":2021},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.093684Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:4d5c428a12e8f081d7b4d53c731a2eac78687f5141d13ea8c6f67da3634d23b1","observation_id":"34ae0559-1d8e-489f-9e17-3e8d471f4cf8","resolution":{"observed_at":"2026-08-10T14:40:13.810434Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.097678Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.097678Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:88fed4cf3f2ee6275421946769cfd145f4c06828303daacd1ce61abb8cfa78b4","observation_id":"27a038a9-66db-49e8-a669-d927615d7474","resolution":{"observed_at":"2026-08-10T14:40:13.097678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07988","last_updated":"2021-10-26T20:07:59Z","snapshot_observed_at":"2026-08-14T06:24:06.038319Z","submitted_at":"2020-06-14T19:27:39Z","title":"Adaptive Universal Generalized PageRank Graph Neural Network","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07988","snapshot_observed_at":"2026-08-10T14:40:13.101740Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.101740Z"},"links":{"cited_paper":"/paper/2006.07988","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:b42e3cdd2d44876b438428bc6bfe70f12e49518cf0092f5fae34395f37b749cf","observation_id":"d18b972a-c599-4bdb-9370-0c3fd2656b80","resolution":{"observed_at":"2026-08-10T14:40:13.101740Z","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":"2026-08-10T14:40:13.784775Z","title":null,"venue":null,"work_id":"034081b1-31b0-4090-b3fd-b3f3ee9a44d7","year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.105976Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:cbc3518218f6109e36c563e9ef0d8b786eb1844ac3360f5a49914ca6e08c9066","observation_id":"fab63a23-1a2d-41e6-9ed7-91e406d4796a","resolution":{"observed_at":"2026-08-10T14:40:13.789320Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.110084Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.110084Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:75023a2efee99ec4d4969cbb1aeeed516baa2e74b93bfeacc6e1e3cd531fe734","observation_id":"16f462ad-79f9-41ac-884f-deee508cc4c6","resolution":{"observed_at":"2026-08-10T14:40:13.110084Z","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":"2026-08-10T14:40:13.114375Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.114375Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:0ca902f1e838156c7775ce8c69c20b347bbdf0e59a94dfb6af6c0f93aff63131","observation_id":"b4e15a92-9b0d-44d1-9e00-19f9be9ea4f6","resolution":{"observed_at":"2026-08-10T14:40:13.114375Z","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":"2026-08-10T14:40:13.117863Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.117863Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:fb0ac7d798ca0be9a39da46be14870b2fbade373b07a5f91e00092670c6e25c4","observation_id":"2fde0351-eb8b-4c26-8fc2-217734538424","resolution":{"observed_at":"2026-08-10T14:40:13.117863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-17T18:04:53.578114Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-10T14:40:13.121468Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.121468Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:02fe3010db81014fd03b839f40187f99215545609b853c2493c93f207b74c299","observation_id":"3c198117-b0fe-4192-bf3b-a83b18b49caa","resolution":{"observed_at":"2026-08-10T14:40:13.121468Z","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":"2026-08-10T14:40:13.125202Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.125202Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:31c8054f525b738994c42d2db5266d161e8b50d24c0ec2273ac17921851af3c4","observation_id":"6c32625a-4a60-474c-a27b-a79a3c6dcbff","resolution":{"observed_at":"2026-08-10T14:40:13.125202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-10T14:40:13.129155Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.129155Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:cd2b59377faadd889c49faa7478d524345ca4e0ba4f10972bb788d886ba96cb1","observation_id":"5294a2ef-8a9a-48ad-ab23-5f914946eba8","resolution":{"observed_at":"2026-08-10T14:40:13.129155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.07308","last_updated":"2016-11-21T11:37:17Z","snapshot_observed_at":"2026-08-14T21:29:27.927932Z","submitted_at":"2016-11-21T11:37:17Z","title":"Variational Graph Auto-Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.07308","snapshot_observed_at":"2026-08-10T14:40:13.133406Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.133406Z"},"links":{"cited_paper":"/paper/1611.07308","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:6971a82164bdba2f198c8918255af6d267c37374de4dc29795580e63b27754b5","observation_id":"c72e6de1-3a91-4d81-8132-f0f6475d26cc","resolution":{"observed_at":"2026-08-10T14:40:13.133406Z","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":"2026-08-10T14:40:13.744072Z","title":null,"venue":null,"work_id":"84d31544-aa98-4134-bb04-d322528933d4","year":2012},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.137475Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:791fee6938d45e346db65039ad85632abb690743452f3e79608542973d4910ad","observation_id":"6c123352-8f11-4f10-afd9-3cbb80e0926a","resolution":{"observed_at":"2026-08-10T14:40:13.748162Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.732598Z","title":null,"venue":null,"work_id":"d48d4713-2225-46ac-baca-2d4a98abd054","year":2022},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.141212Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:9f528fabeae48a5d346c5fbef266ac0bc1b923d1ae56f3d16f62638436ff377b","observation_id":"c441bd7e-c2ea-409b-9d97-921525b41e46","resolution":{"observed_at":"2026-08-10T14:40:13.736424Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.721260Z","title":null,"venue":null,"work_id":"045f0019-3e05-4b8c-a233-4d66e87c3e50","year":2021},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.144722Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:25824a418b87ec9aa63de11333454d4fbd0d0c240a95d7252cffe976e56981f7","observation_id":"eee0e8b6-3709-4250-b835-5905321cfb57","resolution":{"observed_at":"2026-08-10T14:40:13.725270Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.148275Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.148275Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:0ca9124611f46cf10e2fa48b81c58addf8142c5cc8e28c32d5df9d5be2cc69eb","observation_id":"6d37d595-b493-48dd-be0b-271738b0139a","resolution":{"observed_at":"2026-08-10T14:40:13.148275Z","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":"2026-08-10T14:40:13.703006Z","title":null,"venue":null,"work_id":"56e24b90-a386-44c0-a849-269cadff3b68","year":2021},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.151731Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:3175a8827899389e8868c526785dda759c86b1f3c2f87310f38f940f6c557557","observation_id":"e43fc787-64d6-4021-ab30-7144f4abba0c","resolution":{"observed_at":"2026-08-10T14:40:13.706781Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.692133Z","title":null,"venue":null,"work_id":"3e70bd85-a843-44a1-97f8-7c6a23775616","year":2022},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.155261Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:34fb20b5a38aec159c153af5c76675a40c100fb7c28aacc5f58de28b3c89c872","observation_id":"d1fce65d-6d5c-419f-b2ce-7e976b954698","resolution":{"observed_at":"2026-08-10T14:40:13.695863Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.680851Z","title":null,"venue":null,"work_id":"04b210f5-01f7-40ba-8003-fe656ce8530e","year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.158793Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:a02dbf11dd549a0c632f4d414ad3ba7a7e323a7267faec849f0cc84ddb0b53e6","observation_id":"03597d9e-25aa-41bb-8f27-7969c3b43926","resolution":{"observed_at":"2026-08-10T14:40:13.684494Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.162854Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.162854Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:d26e25775646378da3bd17de078154ce11df42ba3f2750771cecb81e7bdf1583","observation_id":"ae2f1941-8aa3-42f6-9b91-7766932c0c6b","resolution":{"observed_at":"2026-08-10T14:40:13.162854Z","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":"2026-08-10T14:40:13.166272Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.166272Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:573a6b64fc794eefd874f7301a2e92131dcc68941274a5e9adb0372d04f6b007","observation_id":"ffa00017-8cf9-4922-b577-92596638372b","resolution":{"observed_at":"2026-08-10T14:40:13.166272Z","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":"2026-08-10T14:40:13.174952Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.174952Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:393465beafe2fc0131710062faa677343edff04491133153421eb8868f12b5c3","observation_id":"28b77323-7212-4693-872e-71bc5fb2f552","resolution":{"observed_at":"2026-08-10T14:40:13.174952Z","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":"2026-08-10T14:40:13.178698Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.178698Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:62ac1a82a27e4481a3325e66a67feaea257170edf69bfcfc23eb64a18d6dbb2f","observation_id":"87596224-6965-4faa-9394-b63dc81f664c","resolution":{"observed_at":"2026-08-10T14:40:13.178698Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.01000","last_updated":"2020-01-17T16:20:00Z","snapshot_observed_at":"2026-08-18T22:53:19.441314Z","submitted_at":"2019-07-31T06:28:43Z","title":"InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.01000","snapshot_observed_at":"2026-08-10T14:40:13.182350Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.182350Z"},"links":{"cited_paper":"/paper/1908.01000","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:d03b4a7f15937262b5b4ea89a79de11838be835eacf541098df36ac6f258e1d6","observation_id":"8f491be6-b190-45cf-83e2-0510e34ac2c1","resolution":{"observed_at":"2026-08-10T14:40:13.182350Z","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":"2026-08-10T14:40:13.186560Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.186560Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:85c39d576eb173f4a134bad11ab19ca6597135cd395db1e3cd2cdd5d3080ad82","observation_id":"7692c481-d6fe-44f8-bd11-e56ac857b6c4","resolution":{"observed_at":"2026-08-10T14:40:13.186560Z","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":"2026-08-10T14:40:13.190251Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.190251Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:279c352ccf1713d549227a686741b2d55f456f1d136354e084901b8d01c7ba1b","observation_id":"70d7380b-890e-4134-ab82-05348646ea06","resolution":{"observed_at":"2026-08-10T14:40:13.190251Z","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":"2026-08-10T14:40:13.194656Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.194656Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:e5a5cc7ce7f2183b20eff4ddd06dfd595b016db41318ac60dc9e3131acb84dbe","observation_id":"4bb6e5fe-323f-440a-a60b-dbecebf01d80","resolution":{"observed_at":"2026-08-10T14:40:13.194656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-08-19T00:46:49.288994Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-08-10T14:40:13.198464Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.198464Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:1fe45837d16bf2d71591bac57c1de75865e53797de8ed187272a00d02a06c178","observation_id":"db68d468-4cc6-457d-baf8-5eb4dae4b641","resolution":{"observed_at":"2026-08-10T14:40:13.198464Z","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":"2026-08-10T14:40:13.202609Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.202609Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:8e3313a5dd4adddc6ce3d4ff28eecf2a0d57acaf53382b42feec4af070639def","observation_id":"fc373273-335a-49c7-ac9f-15f3f0efd267","resolution":{"observed_at":"2026-08-10T14:40:13.202609Z","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":"2026-08-10T14:40:13.613730Z","title":null,"venue":null,"work_id":"03fe35ce-cb92-4ca2-a696-a45d653f8196","year":2023},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.206562Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:1176f2bb186ccc1af4fccfc2579e1eb2f8d55d066107eb60a86b5d1937785ca2","observation_id":"3ccef377-64cf-46f5-a3c3-3106b74d4989","resolution":{"observed_at":"2026-08-10T14:40:13.617468Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.601804Z","title":null,"venue":null,"work_id":"3b6c2a3c-5cc8-459a-8cf0-614c21af47f7","year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.210291Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:609f8179a84030d44e9cc006e164a34428a2489a8fa80254b9dbc23ddf5df32b","observation_id":"8fdbd4a1-6e78-4c28-beb7-498bca6bf131","resolution":{"observed_at":"2026-08-10T14:40:13.605720Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.213975Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.213975Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:757cf0060ecbbc1b7b5c1098b6076c1fc25eed6b2594f75f9e8421e55f838507","observation_id":"55e8bd0f-786b-4b00-9704-47b70db6ab6e","resolution":{"observed_at":"2026-08-10T14:40:13.213975Z","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":"2026-08-10T14:40:13.217842Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.217842Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:58027d3331b0e6ff681e132457e645758ac4a71f9b5b31926ce3b70bc05d1b69","observation_id":"b5b56ea4-7343-49d5-a646-2a1f81db082f","resolution":{"observed_at":"2026-08-10T14:40:13.217842Z","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":"2026-08-10T14:40:13.576044Z","title":null,"venue":null,"work_id":"a03b1b82-5353-4dd6-bbcc-f840820db728","year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.221601Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:b1c9003cdccd8c1d45962b7c596e5f99cb4f9ab5119bfdc1e650a38ee74ae658","observation_id":"9ea82259-1cc4-4cca-a80d-dc9d3286c54e","resolution":{"observed_at":"2026-08-10T14:40:13.580141Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.225396Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.225396Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:970b1803f562077f6c173a2a686c0d80ce7ccd9233b08636afcfe01d17a08ecc","observation_id":"c64b1dc8-77fb-4284-a8db-2a28b0fe5ea5","resolution":{"observed_at":"2026-08-10T14:40:13.225396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12594","last_updated":"2025-02-26T06:58:51Z","snapshot_observed_at":"2026-08-16T21:15:54.935680Z","submitted_at":"2024-08-22T17:57:31Z","title":"Non-Homophilic Graph Pre-Training and Prompt Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12594","snapshot_observed_at":"2026-08-10T14:40:13.229460Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.229460Z"},"links":{"cited_paper":"/paper/2408.12594","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:ab8d248ee19b9e31be2f1be79a402818fa57fd3aa31441e5e67bbc45e5829ea9","observation_id":"448f8a7f-5db4-4a4a-b55c-3de21f226f55","resolution":{"observed_at":"2026-08-10T14:40:13.229460Z","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":"2026-08-10T14:40:13.233352Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.233352Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:cdb41598ef53e77ce58b763f8e5095c22a1481e8b95425ea4c82fbb228aa34e3","observation_id":"ef4b754c-7ec3-4d65-afdd-e4bee7442a1f","resolution":{"observed_at":"2026-08-10T14:40:13.233352Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13934","last_updated":"2024-09-22T03:47:19Z","snapshot_observed_at":"2026-08-16T13:50:29.860649Z","submitted_at":"2024-05-22T19:06:39Z","title":"Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13934","snapshot_observed_at":"2026-08-10T14:40:13.237609Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.237609Z"},"links":{"cited_paper":"/paper/2405.13934","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:9ad3cf798af4aa73d3a8b673cb92192302d660db29ee59006d1bee12640f10f5","observation_id":"4e36eca0-620c-4e39-9897-94fe2b4818c2","resolution":{"observed_at":"2026-08-10T14:40:13.237609Z","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":"2026-08-10T14:40:13.241833Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.241833Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:1e21e0a9205b7b89899da8c86a5774c05b6e4e266f053fdece3060cf07700354","observation_id":"659f3462-7ce5-471e-99af-4c32a2d92149","resolution":{"observed_at":"2026-08-10T14:40:13.241833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09834","last_updated":"2024-06-22T13:29:36Z","snapshot_observed_at":"2026-08-16T14:18:22.279679Z","submitted_at":"2024-02-15T09:55:39Z","title":"All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09834","snapshot_observed_at":"2026-08-10T14:40:13.245479Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.245479Z"},"links":{"cited_paper":"/paper/2402.09834","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:3644d2f819913fe707b5fc66df80ece7431b8d83b69d7501d273f08a33ef68ee","observation_id":"ca75ef56-e59e-4266-80f6-86e06b35cd75","resolution":{"observed_at":"2026-08-10T14:40:13.245479Z","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":"2026-08-10T14:40:13.543141Z","title":null,"venue":null,"work_id":"378bd9f9-8a46-43ae-868e-684f1fda576c","year":null},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.249461Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:006b24364ffb3bd9a6368bcbdc716ae65a4a6507adf6a89dacd22b73221d8f08","observation_id":"3fde8f75-a4d2-4b76-843e-0a7ce11c9d79","resolution":{"observed_at":"2026-08-10T14:40:13.546938Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T14:40:13.257625Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.257625Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:1d6c026e9eeb21d162ba7fe8b099191202ec36392d79fb6b817da908182e1378","observation_id":"a2b59dd1-376c-4fdb-84e0-eef2f78cdb20","resolution":{"observed_at":"2026-08-10T14:40:13.257625Z","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":"2026-08-10T14:40:13.511308Z","title":null,"venue":null,"work_id":"8ed72ee9-7bb9-4218-b765-ec0050b50a78","year":2024},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.261693Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:ac11e32a370d61f8102638d31ae7ff51387c514d4042357bc8bd593a5e380c42","observation_id":"d625f953-c0c5-477f-a75a-9f4d30554a1e","resolution":{"observed_at":"2026-08-10T14:40:13.515127Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-18T19:43:01.466508Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-10T14:40:13.265458Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.265458Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:e585ce93508ced1b6271baf85cb50783d7cdee75d0128b8dbe38b3dd15d614f0","observation_id":"b3e1ef5e-ea9c-4b8c-96f2-4aecb3e37c90","resolution":{"observed_at":"2026-08-10T14:40:13.265458Z","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":"9680.9760","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:40:13.365962Z","title":"Labels for Texas, Cornell, and Wisconsin represent web page cate- gories, while Chameleon and Squirrel labels capture average monthly web traffic, grouped into five ranges","venue":null,"work_id":"6de186a2-6146-4c2a-af67-974e9faa6813","year":2025},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.270083Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:38b8bae6a71b818b049dd9a1794253efb266594de4dbae1a4ac5a31953af8798","observation_id":"7a8eb3a9-1c34-40fd-8d8a-179e8343233e","resolution":{"observed_at":"2026-08-10T14:40:13.375369Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05287","last_updated":"2020-02-14T01:47:35Z","snapshot_observed_at":"2026-08-10T23:39:48.575656Z","submitted_at":"2020-02-13T00:03:09Z","title":"Geom-GCN: Geometric Graph Convolutional Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.05287","snapshot_observed_at":"2026-08-10T14:40:13.170963Z","title":"arXiv preprint arXiv:2002.05287 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.170963Z"},"links":{"cited_paper":"/paper/2002.05287","citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:ad3502771b824ecb450f9b955e9fe1ab986567676558833376f63be4a288cbaa","observation_id":"5983e6a8-085e-499e-87ec-a4186d2f024a","resolution":{"observed_at":"2026-08-10T14:40:13.170963Z","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":"2026-08-10T14:40:13.530797Z","title":"In The eleventh international conference on learning representations","venue":null,"work_id":"79c1be24-363f-4a78-8277-354981cb424c","year":null},"citing_paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-10T14:40:13.253314Z"},"links":{"citing_paper":"/paper/2501.15142"},"observation_digest":"sha256:2fd16c4646ee4e22a7fd13074b8687b5933bcda4e1fd41aeff0e675c3521b382","observation_id":"bb642059-43b0-48e2-b071-d098bc22d011","resolution":{"observed_at":"2026-08-10T14:40:13.534966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.15142","last_updated":"2025-01-25T08:53:42Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T21:05:57.605772Z","submitted_at":"2025-01-25T08:53:42Z","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":45,"verified_exact":1,"verified_fuzzy":1},"total_outbound_references":47},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2501.15142."}