{"as_of":"2026-08-19T15:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e470ee03824211928bdf5c974ab340fbbd9d57ccbfec10ec7a2696ded4d48b48","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:06:15.363053Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2505.08168/citation-record","integrity":"/paper/2505.08168/integrity","json":"/paper/2505.08168/citation-record.json","paper":"/paper/2505.08168"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.04668","last_updated":"2024-02-24T06:44:45Z","snapshot_observed_at":"2026-08-16T14:54:18.914340Z","submitted_at":"2023-10-07T03:14:11Z","title":"Label-free Node Classification on Graphs with Large Language Models (LLMS)","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04668","snapshot_observed_at":"2026-08-15T22:06:15.228513Z","title":"Label-free node classification on graphs with large language models (llms)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.228513Z"},"links":{"cited_paper":"/paper/2310.04668","citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:1fd2cb9eafb4513329a911e27720a5a40eb4bc776863edeb7ccbfa3bcbf033d0","observation_id":"ab77a6e5-3821-4ca6-a0bb-3d0695941ce0","resolution":{"observed_at":"2026-08-15T22:06:15.228513Z","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":"2026-08-15T22:06:15.763514Z","title":"Graph neural networks for recommender system","venue":null,"work_id":"0e723f3e-1497-498b-b042-a628785b816b","year":2022},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.252998Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:9cfc1718252390eeddd3979fb8a6a28e58a5e319d88f69128ce7ff4f1e8aec56","observation_id":"48917853-8be8-4eb3-b0cf-ee9e3de722e1","resolution":{"observed_at":"2026-08-15T22:06:15.766999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.730488Z","title":"Momentum contrast for un- supervised visual representation learning","venue":null,"work_id":"b0369f7b-344f-4b8e-b9b6-e128bd61b27f","year":2020},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.263009Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:9be3d57241d30d44d3655891ded0d63f1e9126a045acdc8153d84c42274ad857","observation_id":"8e2c1ef3-28f2-4b57-9422-7382a5ef97db","resolution":{"observed_at":"2026-08-15T22:06:15.734351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.719173Z","title":"Gpt-gnn: Genera- tive pre-training of graph neural networks","venue":null,"work_id":"d058a044-4397-4c82-a06c-3ed6cd1f42f6","year":2020},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.266209Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:4b2823dcd93f9d167f4cd06271984b4787fc6f20d8e735c2794e73cfe635c49d","observation_id":"b640c066-8f86-43f4-8024-e1f4edb6ff55","resolution":{"observed_at":"2026-08-15T22:06:15.722859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.02848","last_updated":"2023-09-06T09:12:52Z","snapshot_observed_at":"2026-08-18T23:07:22.844027Z","submitted_at":"2023-09-06T09:12:52Z","title":"Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.02848","snapshot_observed_at":"2026-08-15T22:06:15.269440Z","title":"Prompt-based node feature extractor for few-shot learning on text-attributed graphs","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.269440Z"},"links":{"cited_paper":"/paper/2309.02848","citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:207ff309be9e96100560648f2f0ac8ec0b7ba43645dcf4251179c7735d64d13c","observation_id":"a0b962f1-e8f6-4328-be79-90f02ab07e0a","resolution":{"observed_at":"2026-08-15T22:06:15.269440Z","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":"2026-08-15T22:06:15.273233Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.273233Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:149689f1db01c5fa2cd6f4df5c8dd0506b18062b10b57a6c7360441fda6ed3db","observation_id":"a0e3cacc-18ce-4635-866f-18f26d9c0e76","resolution":{"observed_at":"2026-08-15T22:06:15.273233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-08-16T14:33:50.657682Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-15T22:06:15.283636Z","title":"Roberta: a robustly optimized bert pretraining approach","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.283636Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:eaaa250ab4c507739023468108c88294b30aa4f90f0ee7f9180cb683d0383a9c","observation_id":"fa1b55b7-c187-41bb-b6ac-c96b061c8da5","resolution":{"observed_at":"2026-08-15T22:06:15.283636Z","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":"2026-08-15T22:06:15.673271Z","title":"Few-shot node classification on attributed networks with graph meta-learning","venue":null,"work_id":"549ed014-44f0-46dc-831a-490d8911315f","year":2022},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.290648Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:98bec10357c801c042f56d6b74c6143130d587eaa35e60bbd61b206af14ae1e2","observation_id":"4471f9b1-75d7-4093-881d-d9e989c3ed0a","resolution":{"observed_at":"2026-08-15T22:06:15.677134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.293931Z","title":"Graphprompt: Unifying pre-training and downstream tasks for graph neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.293931Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:235ae181cca761c773bc3ea48bcfaa1a88bc4b2cadfe1f202935bf0dbda32ff6","observation_id":"17f93b04-436a-44d4-9e8c-704fdebf774d","resolution":{"observed_at":"2026-08-15T22:06:15.293931Z","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":"2026-08-15T22:06:15.654157Z","title":"Automat- ing the construction of internet portals with machine learn- ing","venue":null,"work_id":"e13e605f-c11d-4b08-8c9e-5cc883b87530","year":2000},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.297284Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:e1c6a86e24bab398368ed4008aee6bf936e38f398696e2fe8fe518fc2ea916ad","observation_id":"68b324d4-266b-4940-b382-c0a8c4dacd94","resolution":{"observed_at":"2026-08-15T22:06:15.658712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.625570Z","title":"Graphgpt: Graph instruction tuning for large language models","venue":null,"work_id":"fa7fb986-f7ed-4ad9-a176-327ceb953774","year":2024},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.307199Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:75377b6699a032a61c4c9ff8c255476ec3e740eea4fa565c51beb1b5d6cde909","observation_id":"7639a4cf-7bc0-48a6-8ab4-1e973332145a","resolution":{"observed_at":"2026-08-15T22:06:15.629289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.616150Z","title":"Attention is all you need","venue":null,"work_id":"c9b46c77-d752-4c10-864e-a8464fcd3a57","year":2017},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.310637Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:93f992f92b08334726d70e49e23c486eaf6df45f381f69fdb022435790dbe206","observation_id":"6785473c-2421-4f6a-8b48-d3eeb7e56e9b","resolution":{"observed_at":"2026-08-15T22:06:15.619489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-08-13T22:35:40.714745Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-15T22:06:15.313804Z","title":"Graph attention networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.313804Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:c3fc753fe427cc9af3829aadc899790fd1663ca847e95be3fcc8a613d1fe693e","observation_id":"b9325a54-cb29-4ea5-8e53-faa7ffbe74d8","resolution":{"observed_at":"2026-08-15T22:06:15.313804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.10341","last_updated":"2018-12-21T15:44:59Z","snapshot_observed_at":"2026-08-14T18:22:57.330140Z","submitted_at":"2018-09-27T04:53:24Z","title":"Deep Graph Infomax","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.10341","snapshot_observed_at":"2026-08-15T22:06:15.317226Z","title":"Deep graph infomax","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.317226Z"},"links":{"cited_paper":"/paper/1809.10341","citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:1f022c7974ef88f6e613027811666a158e1b08f93136e9d65873ada99af2b274","observation_id":"e45be443-ac52-412f-bfad-0e603a5a65fd","resolution":{"observed_at":"2026-08-15T22:06:15.317226Z","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":"2026-08-15T22:06:15.606157Z","title":"Generative and contrastive paradigms are complementary for graph self-supervised learning","venue":null,"work_id":"149ee126-7ad6-4e3b-835b-4c06c26f2efa","year":2024},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.320656Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:84be51f5522d9fe18121c62256980821faee46bbc2d409071a0da8b2d2603be7","observation_id":"c4034c7b-bb23-412b-943b-b156bc861a1e","resolution":{"observed_at":"2026-08-15T22:06:15.609990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.595868Z","title":"TESA: A trajectory and semantic-aware dy- namic heterogeneous graph neural network","venue":null,"work_id":"8965291c-b2fe-4ed7-8702-60c0777b5c5e","year":2025},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.324229Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:38c85998e18d606d819d90f91a6bedc8d13a10d8b052a280c536842f9a015a5e","observation_id":"c61247d2-8a83-42b7-85f0-263c0226cba0","resolution":{"observed_at":"2026-08-15T22:06:15.599523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.585585Z","title":"Aug- menting low-resource text classification with graph- grounded pre-training and prompting","venue":null,"work_id":"b847efb7-aaf9-40f2-be71-b47c2a0a63a1","year":2023},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.327623Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:bfb459a3f91ffaa5b05737dbc50b16ffc3392fcef1cc89a8701fa18e807de5c7","observation_id":"d3b98b45-8f00-4ed4-840e-3232d627a32b","resolution":{"observed_at":"2026-08-15T22:06:15.589285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.574622Z","title":"Infogcl: Information-aware graph contrastive learning.Advances in Neural Information Processing Systems, 34:30414–30425,","venue":null,"work_id":"d227c2f1-2490-4cc7-9372-938fde732583","year":2021},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.330632Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:32291659a73e6a75291f19a6c99bd2246eb163051eebf229d48e41157e515c2b","observation_id":"8a35a2b0-ffb3-44db-b233-4858c2f001b6","resolution":{"observed_at":"2026-08-15T22:06:15.578374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.563395Z","title":"A comprehensive study on text-attributed graphs: Benchmarking and re- thinking","venue":null,"work_id":"88b241b7-7555-49d2-b37f-9d5aa60e4be3","year":2023},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.334003Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:319c0a29bdd5bdabb183b5be7a5d8d43112001525ddc18f0cef51e00dcf7e5e2","observation_id":"b2464dcb-5e5c-4668-b528-a65076918fbc","resolution":{"observed_at":"2026-08-15T22:06:15.567433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.552453Z","title":"Oceanbase: a 707 million tpmc dis- tributed relational database system","venue":null,"work_id":"5648f014-4063-48b9-ae00-06a692beafa3","year":2022},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.337261Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:85204bfa68cbfafd8b466e80ba1a53d66d56d02daf29b499fb4eccf4ca69650d","observation_id":"58794768-60e0-46c3-b308-668b519f4509","resolution":{"observed_at":"2026-08-15T22:06:15.556020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.541646Z","title":"Oceanbase paetica: A hybrid shared-nothing/shared- everything database for supporting single machine and dis- tributed cluster","venue":null,"work_id":"e4c26dcf-b381-4b29-bb70-f84ac0ec13cd","year":2023},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.340413Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:9ffc7df4ce55adb72a70c662c41be86e1588d79ca04f39e9243f7c2c6ca6c710","observation_id":"80429b8f-4600-4354-935a-3cd8cebd14f3","resolution":{"observed_at":"2026-08-15T22:06:15.545703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.530941Z","title":"Graph convolutional networks for text classification","venue":null,"work_id":"18a41ae5-efea-4ccc-bab7-cbea3f919a3c","year":2019},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.343698Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:4139910bb5d749583fb12415341d07f724741802af546a86849e0327f284b13a","observation_id":"22b90f48-7e25-4c6e-97d6-eac5c94903b4","resolution":{"observed_at":"2026-08-15T22:06:15.534777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.519835Z","title":"Graph con- trastive learning with augmentations","venue":null,"work_id":"06cceeba-b9b3-47fe-820c-52ec206569ca","year":2020},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.346940Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:77094b8f297d5cf76b4272af722e654b70c5b54f0bad70f6428da5af57b2b92f","observation_id":"0b33371c-6b99-46eb-a3e7-a9336791d598","resolution":{"observed_at":"2026-08-15T22:06:15.523554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.509974Z","title":"Enhancing social recom- mendation with adversarial graph convolutional networks","venue":null,"work_id":"e5de8b72-1a54-49b7-818e-b88622b85150","year":2020},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.350083Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:67180658ff2434b07925a574b03a870f4916fdaf5b790457baa9b0f418f67fcb","observation_id":"250b28bb-3609-4bf3-937c-e62fa6edfa36","resolution":{"observed_at":"2026-08-15T22:06:15.513405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.499322Z","title":"Graphtranslator: Align- ing graph model to large language model for open-ended tasks","venue":null,"work_id":"d0360a09-9c88-4047-9568-2da72e5ffafa","year":2024},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.353476Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:d96998aa2a58a4691aaf3a23d5cbbffa7127d692731409a84ccc12d4e9d4322c","observation_id":"5976d061-70ce-4648-abe3-dfd8092c227c","resolution":{"observed_at":"2026-08-15T22:06:15.502966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.487795Z","title":"Tgraph: A tensor-centric graph processing framework","venue":null,"work_id":"b8bc678d-3a3a-4b9e-99c2-977374f16284","year":2025},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.356699Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:01bf2185faed59124ff4c6518509c88f4ab1ded393e7e6fc48458608325d1caa","observation_id":"e372c414-07b2-4de8-bb17-de29567ea465","resolution":{"observed_at":"2026-08-15T22:06:15.491958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15431","last_updated":"2024-07-22T07:24:21Z","snapshot_observed_at":"2026-08-16T13:32:14.220950Z","submitted_at":"2024-07-22T07:24:21Z","title":"Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs","version":1},"cited_work":{"arxiv_id":"2407.15431","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.15431","snapshot_observed_at":"2026-08-15T22:06:15.392105Z","title":"Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs","venue":"cs.SI","work_id":"61cd452e-b945-456d-8bca-540b1336380a","year":2024},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.359841Z"},"links":{"cited_paper":"/paper/2407.15431","citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:f066c05463845a94e427f1c89b6d9a39d9e3c5d18e21ca55638e4c62dab7ab4f","observation_id":"2150c60a-dfa8-4712-abe8-0c3883f79a3c","resolution":{"observed_at":"2026-08-15T22:06:15.398269Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.476432Z","title":"Conditional prompt learning for vision-language models","venue":null,"work_id":"6d48fa09-5b85-4979-84c0-4b8224db50e4","year":2022},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.363053Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:0923ec69b8a9425bcc9549ee56b08aa245c2055f01b7caf36a7395358eac4550","observation_id":"b972cfca-09af-4ed7-8d49-4f17b0fea5f3","resolution":{"observed_at":"2026-08-15T22:06:15.480167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.642441Z","title":"Graph-based anomaly detection","venue":null,"work_id":"80d99149-6ee2-4341-b448-65e4a23254ca","year":2003},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2000,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.300962Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:d5405083b69769177046018dd0f92f1d818959afafde6642d18449df80dad582","observation_id":"3829312f-b067-47c1-b942-58658aa6ffcb","resolution":{"observed_at":"2026-08-15T22:06:15.646397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.303941Z","title":"Gppt: Graph pre-training and prompt tuning to generalize graph neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2003,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.303941Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:5fae72e241c0fb9ee7f04f9b7d9495e95d2e84d79c43d487c52a85ece092ad85","observation_id":"34814b95-511c-4d79-b3b8-b84edc63a624","resolution":{"observed_at":"2026-08-15T22:06:15.303941Z","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":"2026-08-15T22:06:15.708352Z","title":"Towards scalable and deep graph neural networks via noise masking","venue":null,"work_id":"8417aa52-7eca-41d4-bc5f-e00ed2255d9d","year":2025},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.277069Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:34776997b7b24e827530b3acd633b3ec34ec1e95d4d0f8be8c4d31706e5852af","observation_id":"5f0eea04-7086-413c-80c0-c5669cf07359","resolution":{"observed_at":"2026-08-15T22:06:15.712250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.741887Z","title":"Palf: Replicated write-ahead logging for distributed databases","venue":null,"work_id":"c39e6007-d053-417d-9660-4c75d584cd4f","year":2024},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.259829Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:0943477f27993b584e319427ccead61233bf1c9129e2e7abdbec8e120a823256","observation_id":"45092e59-36cb-488b-ad66-18fe271d0d5b","resolution":{"observed_at":"2026-08-15T22:06:15.745611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.249065Z","title":"Universal prompt tuning for graph neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.249065Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:af24775b6374ecacb3d50d125589766d00e9e18a30f719437af74186bd9882d5","observation_id":"cfa92904-feca-4235-971e-e3a1d28aeda1","resolution":{"observed_at":"2026-08-15T22:06:15.249065Z","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":"2026-08-15T22:06:15.685348Z","title":"Relative and absolute location embed- ding for few-shot node classification on graph","venue":null,"work_id":"94d1f5c6-9616-42ca-9d21-c59c979410aa","year":2021},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.287228Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:0a7f516809f1cda42da67537df26a02a234822467beab655993a26f4457c98e7","observation_id":"2a06c677-6c52-478c-8751-fcf0eb93d78e","resolution":{"observed_at":"2026-08-15T22:06:15.689448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.780905Z","title":"Graph neural network-based anomaly detection in multivariate time series","venue":null,"work_id":"1dfe82d2-e419-4a85-8000-54ab711eed15","year":2021},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.241054Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:4362ab78ab5e4d713c6187b8be2c2745f02b89dea70a465fbe55fe09c3dc41aa","observation_id":"663b589d-b409-4665-bce4-ab07d3af02ef","resolution":{"observed_at":"2026-08-15T22:06:15.784790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-15T22:06:15.245030Z","title":"Bert: pre-training of deep bidirectional transformers for language understand- ing","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.245030Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:8ad67989ecc51f5570b0dfe0c6082697443ee6bfdf03217b3b79a903081fb7b0","observation_id":"846b18c3-45de-4ced-a116-05da68280f4b","resolution":{"observed_at":"2026-08-15T22:06:15.245030Z","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":"2026-08-15T22:06:15.752881Z","title":"Inductive representation learning on large graphs","venue":null,"work_id":"f18b35a3-c9f9-446c-97bd-c1a60d360168","year":2017},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.256382Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:50e16dfbe904956119ae0bbd48401a1b575759b40e1262fd7da3d2ead5d4839e","observation_id":"4056557a-0271-4c4f-9d99-66444d9736c8","resolution":{"observed_at":"2026-08-15T22:06:15.756639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.233495Z","title":"Exploring the potential of large language models (llms) in learning on graphs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.233495Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:f35092d4fbb6cbb8006ad08d5c553ca47c1515df94b8742968172c898c324c79","observation_id":"28187ede-f933-4bb2-bb23-32f3f8efa97d","resolution":{"observed_at":"2026-08-15T22:06:15.233495Z","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":"2026-08-15T22:06:15.791557Z","title":"Debiased contrastive learning","venue":null,"work_id":"79e6f534-db75-4c6d-9e04-ad9868d31660","year":2020},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.237020Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:ea01877bafd7fbeaa04625f158c7a826a5b7d68de3087f332c8ab827294cc6c5","observation_id":"30b9ef54-b64a-4c5f-a44d-af8edbcded8a","resolution":{"observed_at":"2026-08-15T22:06:15.795192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-15T22:06:15.697295Z","title":"Prototypical graph contrastive learning","venue":null,"work_id":"05eddf84-05cc-40a8-8678-991c305e9542","year":2022},"citing_paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T22:06:15.280499Z"},"links":{"citing_paper":"/paper/2505.08168"},"observation_digest":"sha256:d3e49a5d575512a013ee7694b8f0996fed36454ed376c3c2f5412f74a32d73f3","observation_id":"e5fdc118-8e16-4951-9ae4-677dfe54b9d0","resolution":{"observed_at":"2026-08-15T22:06:15.701339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.08168","last_updated":"2025-05-13T02:06:08Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-15T21:59:43.916829Z","submitted_at":"2025-05-13T02:06:08Z","title":"Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":40},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.08168."}