{"as_of":"2026-08-14T15:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bb3f09b76d209f7bb1a24ed00fa19f57f61b0104b02f2c6112323be1f084a51d","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-11T02:17:33.913360Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2605.07133/citation-record","integrity":"/paper/2605.07133/integrity","json":"/paper/2605.07133/citation-record.json","paper":"/paper/2605.07133"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Machine Learning Research , volume =","venue":null,"work_id":"d5d44a10-dc64-4755-b703-fb3d4c3156b7","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:fd3d6ead68405c3db6da5e8cdfea8c70663ef325ffc337cd05b9173fc7af2692","observation_id":"9d2d1c37-7633-4178-8577-5dcfd087d136","resolution":{"observed_at":"2026-05-14T13:30:54.846047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tsourakakis , title =","venue":null,"work_id":"e533d10d-ca26-465c-9374-0b208e73b90a","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:341615a62b3ca88f29926db26963f598009e9c3a56e04da4ac4c73f6c9f05e51","observation_id":"00b79a3d-7eeb-4f1c-973a-a84b64a95c56","resolution":{"observed_at":"2026-05-14T13:30:54.844121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the","venue":null,"work_id":"e8a6cdc0-7e21-41f0-9deb-120ff7db034a","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:72f32f1384c4178c3aad483730f452d8d0ad7511f2ad6b46bfc3fdcd798ad4d2","observation_id":"f16d466c-33c4-4d05-9651-16d32c0c0168","resolution":{"observed_at":"2026-05-14T13:30:54.838682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the SIAM International Conference on Data Mining","venue":null,"work_id":"5eff50e7-596c-4e49-8e35-9ea4e7b863f1","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:731b3177531282ae75258d6d8a958550aae7a57c03533f881c5d655c6bf44f1d","observation_id":"05f78314-af0e-4dea-90af-2cbd4fe73963","resolution":{"observed_at":"2026-05-14T13:30:54.768347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the Fifteenth","venue":null,"work_id":"e6956c25-1d93-45f8-8397-1fe8886dccf9","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:198e38d1a33fe37fe125caf0e741e94f189d86486330098ada506c34c25fe8d0","observation_id":"b6af506c-d7bd-44f0-b48e-db31743c4f49","resolution":{"observed_at":"2026-05-14T13:30:54.836866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing","venue":null,"work_id":"02ae94da-c2ea-4e9c-99ab-5c9662faf0cd","year":2020},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:f091a3637005968f605ac0b97224aa85be8b499d2ef2f161c7bb0052a97da5ae","observation_id":"8e07d484-d813-43fb-8d14-08139c9fd4c8","resolution":{"observed_at":"2026-05-14T13:30:54.831542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Kipf and Max Welling , title =","venue":null,"work_id":"3b17b170-64c5-49f3-ac3f-fa6b51aac58c","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:7009aec2df11d5e847186b982985adbffa199d983fd0260087d874cd00eafda5","observation_id":"22ccca40-6661-4d28-badb-113f616468a2","resolution":{"observed_at":"2026-05-14T13:30:54.821870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , year =","venue":null,"work_id":"33d9a781-8206-45f9-a509-f204e29602da","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:ecba0d932a5daf2d91dcd0a0cc92b15d89b7996fae2eb04bc01c34ac8c4705c5","observation_id":"4aa1fc9e-511e-497d-9223-bf97fe4db67f","resolution":{"observed_at":"2026-05-14T13:30:54.842303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Foundations and Trends in Signal Processing , volume =","venue":null,"work_id":"66fcc82b-dc6a-4b37-be6b-e3f38fda20b2","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:707ebef56d0a08640163f09e26206e9b6941b646e093d96fee01bf977fe7178b","observation_id":"221c1aa3-7086-400d-b209-580267bd8264","resolution":{"observed_at":"2026-05-14T13:30:54.776847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4c8b4dcd-5fad-4d03-89ae-e807cf111ac3","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:d26494971d766f53d63ce2c68f237f90797e2fa1233f6fcee2286b8f567c1e0e","observation_id":"0e681191-8862-4e04-bb85-1b37e46b9dbf","resolution":{"observed_at":"2026-05-14T13:30:54.814565Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Das , title =","venue":null,"work_id":"cab4d919-e735-4744-a626-c8acf65c7cab","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:bd391f562e443bf115ec4238adc1e9914219bc0720fa0083acf63d326523d959","observation_id":"4a33f72f-db92-4c47-a59e-cf6ccd15daff","resolution":{"observed_at":"2026-05-14T13:30:54.782435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"IEEE Transactions on Neural Networks and Learning Systems , volume=","venue":null,"work_id":"b22fe577-5118-4bba-8d6e-690e5373be4f","year":2022},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:403375836055e0f92316c40b5ae0004e024f743c4a40365f02a0fcffb56fc5f3","observation_id":"9528c092-87fb-4dfd-8fb6-594d04689533","resolution":{"observed_at":"2026-05-14T13:30:54.807698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Chen and Zhihao Jia and Philip S","venue":null,"work_id":"65ed3091-237c-44f1-8e14-5e9c974a6db9","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:14c64b4e1b8aaa46bae43f51d4de091d4ed6fb9570c2c2627d9694d4f2d60e84","observation_id":"24a1564d-7988-4725-9474-e368469e1d26","resolution":{"observed_at":"2026-05-14T13:30:54.829925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the","venue":null,"work_id":"7434b045-fcc2-4251-84d0-5aa48de4e894","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:40bb3c2cc57b320fba03cbda6fb3a1f10b940e6f821d39cd3a92cfb5bf97f8a0","observation_id":"a9ffec85-2e81-4c0b-8321-be84a29c9a88","resolution":{"observed_at":"2026-05-14T13:30:54.824657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ec14f22c-f65a-49a8-9c77-f585a1469a83","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:28ccccfc481d5234a763e8641c0435afa1af2ac283ca6ad8b7f402b137c735b1","observation_id":"b852cbce-ea51-49cd-aba6-a7771474c74c","resolution":{"observed_at":"2026-05-14T13:30:54.811188Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural Computing and Applications , volume=","venue":null,"work_id":"95da5662-1a20-4161-889c-16202988c441","year":2021},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:f11f37a098a093d8131124abe375270bfb9aebfb32a4f15829574511af10496c","observation_id":"5eefe9c3-bcba-4f40-ae96-e4cc18f5fc10","resolution":{"observed_at":"2026-05-14T13:30:54.833235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the ACM International Conference on Web Search and Data Mining","venue":null,"work_id":"e238fa89-19b6-4846-bd83-450773c02acc","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:2828dd95ea9e3017ccc88f05358577c96f0f84e6b9acddee06b948c16404d0f5","observation_id":"ac94f0cc-1431-4dc9-8b7f-cee32392f64c","resolution":{"observed_at":"2026-05-14T13:30:54.802137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the Seventeenth ACM International Conference on Web Search and Data Mining , pages=","venue":null,"work_id":"29718f69-b3d7-40ad-8edb-5c7d89972be3","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:1630e2bbe6f03e24e5028a77982c56c72be102c916bb87d249020a27b073c1b7","observation_id":"a8f06eac-efc0-4716-a570-30e7bd2a6c2c","resolution":{"observed_at":"2026-05-14T13:30:54.826583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the ACM on Web Conference","venue":null,"work_id":"833c39a7-97b9-4186-a393-ea767ecf0f9c","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:3020eb88b32ae9ae68363b184ca0b3ff7cc560d6943c501a7223325be7470ba7","observation_id":"babd53a7-c60c-4ec6-acb6-035a76609040","resolution":{"observed_at":"2026-05-14T13:30:54.790863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural Networks , volume=","venue":null,"work_id":"eac383aa-6824-4102-8d60-d8d7a9c4b002","year":2025},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:72a8c9b9722b2a83d0220b72467443cb4d8bb4ccabdcf440b8eeba6f55924d4e","observation_id":"8d754283-328d-4680-882e-fb019909d07e","resolution":{"observed_at":"2026-05-14T13:30:54.828307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , pages=","venue":null,"work_id":"aef69775-fdb2-426f-8b5f-d06249d229a1","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:0d5eacbe8c307a93b8dc2a202fc40e98cf86c249a0040b9eda23c776fbfadf1a","observation_id":"e42d132b-d9a1-4813-8b09-223935d69146","resolution":{"observed_at":"2026-05-14T13:30:54.772811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"cfa99979-b7a0-4642-9228-613a38d40e39","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:ccc91ace795e6828b9f2bb765084c6b4445d1220164fefb36e07f7475e1d1681","observation_id":"5bc32242-2216-4c5a-8634-0a6d3df6623e","resolution":{"observed_at":"2026-05-14T13:30:54.816226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining","venue":null,"work_id":"ce4461e8-fd7b-47cc-aab1-f7fccdba3624","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:0536ee4f86f30f1178c4c9e7053d704d1a5f8eac8d35440b343042bf88ec97f3","observation_id":"08776e5e-3d4c-40c1-98f3-ae712c098ee1","resolution":{"observed_at":"2026-05-14T13:30:54.840506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"0a32a62f-0e63-4c9e-9c08-a6bd3c204946","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:120ada14da56b103d3fac39f38a8386edabdfc957dc06fab823ef34e7543b9ad","observation_id":"4c8f28c5-6679-46c4-a20e-6b1de0d71d4b","resolution":{"observed_at":"2026-05-14T13:30:54.792585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"26a15999-996b-4f3d-8c23-857ee04a60d5","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:bdcc395911e3956ae9b28a0ae381a72c214680805c419623823fac68ba554430","observation_id":"1d766688-c1eb-4aac-aae4-3a3f0d9a4c73","resolution":{"observed_at":"2026-05-14T13:30:54.798290Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the NeurIPS 2021 Datasets and Benchmarks Track , year=","venue":null,"work_id":"0a1d4e9a-ab65-45a2-8848-59671bc6f157","year":2021},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:c5e8da0e4bd7d488ae39645712c6fc49631b510d38db90a589e03846dc874adb","observation_id":"56bdcb05-cba3-4e89-863e-5fad0ca10832","resolution":{"observed_at":"2026-05-14T13:30:54.805835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection , journal =","venue":null,"work_id":"08fe7386-6e9f-4a9f-bd7d-147cb9f667d6","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:60b7fe39752573d8352b8063748014fa280b0b8d04e1e713eafc8a768d5817c5","observation_id":"251fccee-898d-49e1-9828-ce414ff439c6","resolution":{"observed_at":"2026-05-14T13:30:54.804030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the International Wireless Communications and Mobile Computing","venue":null,"work_id":"61547bc3-a1a5-473a-b191-dd909e5fb292","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:797c149cb077f2e12596290d4cacf49b0e4484d39dc56cb70fc922120345e582","observation_id":"e70f3a8c-a508-4463-b33e-855d18676581","resolution":{"observed_at":"2026-05-14T13:30:54.774849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the International Conference on Learning Representations","venue":null,"work_id":"b81721a3-5a9b-4e33-a336-c2a2bf931a6a","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:1afe3c9528c1fa75093777d8991d44fdf73352547e5ea69cb06d44fb0a6f3b94","observation_id":"e3764dd4-46e5-4ca8-8c17-4b70dfc65d53","resolution":{"observed_at":"2026-05-14T13:30:54.809505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"da7cd6a8-a94c-4dbc-af6f-496fb523e383","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:1e31afe4388626a1fe2379ae5b94e967b7f64d557ed6eb816bb2f9910894013d","observation_id":"8df2db79-1a5f-4d8f-8aa8-9866e157d4b8","resolution":{"observed_at":"2026-05-14T13:30:54.788412Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"e52423b7-8d11-4ce3-a22e-10237e5982f7","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:88272e4f4743c407441e7fd8ddca0bef18f6b5e8f774588b3b38c856771ebb69","observation_id":"0608fdf5-a440-4de0-a7a1-3190a57848c3","resolution":{"observed_at":"2026-05-14T13:30:54.834895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the","venue":null,"work_id":"315265a9-873f-4bb9-93a7-a1885c1d2d73","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:617a9526451ca4ffa32fba42cb79408c5db4e3e8141d43993b5227924cfe0374","observation_id":"82c15fdd-a3e5-4bea-8776-0092c7cc6f92","resolution":{"observed_at":"2026-05-14T13:30:54.800245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence","venue":null,"work_id":"518ea726-a8e1-48b0-b3ec-dc047d83b347","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:d873e5bdd2b86a11fe997a48f920bfe4c4ebcf949b5c24010f9a125c5f7ca80c","observation_id":"f5172a3c-dab4-4dc5-b8ae-b3fe8fc80061","resolution":{"observed_at":"2026-05-14T13:30:54.780745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Aggarwal , title =","venue":null,"work_id":"3d421a55-2694-4289-a947-615d6a4da5df","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:371933b7442b2f932aada469399b7ac4f6c34dbc088e844e82b4b56c7f46a24a","observation_id":"11f082c7-065f-4ef6-80cc-6a0329f8a210","resolution":{"observed_at":"2026-05-14T13:30:54.770697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the Learning on Graphs Conference , volume=","venue":null,"work_id":"144d6a67-6670-4985-838d-2c611fd8bbb4","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:c8853b73407ec4a5745ccc48995df62e9daa74fbef3a9c2c6bbed39bd32b8845","observation_id":"2b177f7c-bcf7-4816-83c0-09c9a6924b04","resolution":{"observed_at":"2026-05-14T13:30:54.818231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the International Conference on Learning Representations","venue":null,"work_id":"db0ce78c-87fa-4755-8af1-94e7c740f0d1","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:3fd3d740e2f55e6124f0e64cb08758530f9e575e49e6d901af743196e8ba7e5b","observation_id":"00a442ac-2398-465f-896c-26a993d73816","resolution":{"observed_at":"2026-05-14T13:30:54.812851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the ACM International Conference on Information and Knowledge Management","venue":null,"work_id":"aad3e3d4-dddb-4249-a3fe-c312228442af","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:6dcf6673d3c2c205a8704a0ec93f31ab39bce36f2e1255a4a203c52636ee0c56","observation_id":"d19e06cb-bb31-4c28-8a71-2bb2e887499c","resolution":{"observed_at":"2026-05-14T13:30:54.778717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"AI Magazine , volume=","venue":null,"work_id":"de6d2169-c237-40f9-beff-bfbbc3f6f149","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:553c6d0e38d6ec2c69c3381fb9981a7d29d44991d65bf6765f63e991030a1e32","observation_id":"28eb5694-1d3a-41f7-b9ef-8ac5607d147b","resolution":{"observed_at":"2026-05-14T13:30:54.796539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Expert Systems with Applications , volume=","venue":null,"work_id":"76ee8067-172a-45b6-9d5b-7315aac26563","year":2009},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:7a2fb5c4c3d51bad146f751bfd0496b7feebbf86523047de35a4336186cc9803","observation_id":"90d6482e-3cf2-490c-9efb-b73151ba7c51","resolution":{"observed_at":"2026-05-14T13:30:54.820156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining","venue":null,"work_id":"f021c619-1d58-4334-ae5b-334431ac9d5e","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:774618caf8cf060da711068b92ef52b7cd69abaaadedcabb7cb438f31a82da90","observation_id":"1f7faa83-040e-4c26-a852-8b60c4eae666","resolution":{"observed_at":"2026-05-14T13:30:54.786388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"91038d5d-211e-492d-a6d3-b7ceff106064","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:79a59b7ed7aa1208f490cb7374c61f3f36df9428783c6c71f03d4a9dcf970509","observation_id":"571539d6-9cac-4e10-bbb0-c19a3a7b8539","resolution":{"observed_at":"2026-05-14T13:30:54.784284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the International Conference on Machine Learning","venue":null,"work_id":"5cff22fc-ebd5-4399-9846-2ecd3a260ffc","year":null},"citing_paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-11T02:17:33.913360Z"},"links":{"citing_paper":"/paper/2605.07133"},"observation_digest":"sha256:3817eea997e0a2b9b1c9d960aa544edf1ca9c973c14c63e1846e2329888b2e89","observation_id":"596c34d2-1b92-4fe3-b389-b6da3874e7c9","resolution":{"observed_at":"2026-05-14T13:30:54.794435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.07133","last_updated":"2026-05-08T02:10:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T16:39:52.979883Z","submitted_at":"2026-05-08T02:10:51Z","title":"GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":42},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2605.07133."}