{"as_of":"2026-08-19T14:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:398f21e2847e12506a2b87e8210a40eb99a70954a5cec0510cc147397f1e0e0d","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:27:29.877071Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"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/2411.17218/citation-record","integrity":"/paper/2411.17218/integrity","json":"/paper/2411.17218/citation-record.json","paper":"/paper/2411.17218"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:30.387275Z","title":null,"venue":null,"work_id":"1ac5e493-14b8-48bc-a091-2179c69145c1","year":2017},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.701878Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:d9d799ad2f6823933e78c0c3902f2e5509985a412534c9e40d137e0a2c49f05f","observation_id":"e2d2dff9-3cb8-4722-9fac-83554238a111","resolution":{"observed_at":"2026-08-12T12:27:30.390981Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1803.01271","last_updated":"2018-04-19T14:32:38Z","snapshot_observed_at":"2026-08-13T10:37:24.864456Z","submitted_at":"2018-03-04T00:20:29Z","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.01271","snapshot_observed_at":"2026-08-12T12:27:29.706511Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.706511Z"},"links":{"cited_paper":"/paper/1803.01271","citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:0e7aa7d1c90337f1a83069066aa2724426192c0c56f206cc8e015422735a51c1","observation_id":"d4bebdba-8167-475f-8b55-265e13e4215b","resolution":{"observed_at":"2026-08-12T12:27:29.706511Z","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":"2026-08-12T12:27:30.375676Z","title":null,"venue":null,"work_id":"6b3d8c0a-c234-4da5-a083-da1ee159d729","year":2021},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.711541Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:b3ff3ccf826678a70263dba35d3eef5ba0d84b7c6dd55b7d490bd9c21a87a5f6","observation_id":"0f90fa23-2d7c-4380-8d94-6675fe006a8b","resolution":{"observed_at":"2026-08-12T12:27:30.379394Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.363801Z","title":null,"venue":null,"work_id":"45b8f51d-1fbd-407d-873f-9719da743018","year":2020},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.716280Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:1ba25520f5e9cfd7c8ff322c05276aef83c858d44a6f6e14f3d64177a1df760a","observation_id":"a6179e8a-f4be-4599-8b79-1a406e06be57","resolution":{"observed_at":"2026-08-12T12:27:30.367977Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.720597Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.720597Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:4825267c9509542fef86a706eda91f6ef2583ca90657fef90a9367a6dce37ddc","observation_id":"9330b0dc-7ccf-4d38-ad72-f141183d5a63","resolution":{"observed_at":"2026-08-12T12:27:29.720597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:29.724515Z","title":"Carmona, François-Xavier Aubet, Valentin Flunkert, and Jan Gasthaus","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.724515Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:7a994854c78fcc7771cf3d3cf3c38a02e9c79ca56dbd18543da6915728573c12","observation_id":"aa44c935-de26-4900-b3c0-7dbca64d611d","resolution":{"observed_at":"2026-08-12T12:27:29.724515Z","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":"2026-08-12T12:27:30.324429Z","title":null,"venue":null,"work_id":"6fd108e4-70b9-43be-a027-be3e65c28066","year":2015},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.732482Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:c37c12fb7bb0a47116faf74683ae1e1bdf5bf9ac1cbf1486ac79a4424e2a872b","observation_id":"b7978744-05a9-4311-a326-06738b676d88","resolution":{"observed_at":"2026-08-12T12:27:30.328507Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.736480Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.736480Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:83f132078456f07873c13385505ceae293248df418411b2c8b19905617a0c416","observation_id":"73179858-8310-4f61-8e9c-458dd26160a7","resolution":{"observed_at":"2026-08-12T12:27:29.736480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:29.744247Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.744247Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:c66e1b977b006c9c7174e33a8db5e29bba5dcfb44d461fb2d0d2cff8a5f723b5","observation_id":"c41dbac2-f54f-46fa-b38e-207b30cab6af","resolution":{"observed_at":"2026-08-12T12:27:29.744247Z","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":"2026-08-12T12:27:30.286644Z","title":null,"venue":null,"work_id":"0318bcb8-d9be-48ce-a762-5bf5e041804c","year":2021},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.748027Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:f1c7dd6bc2de1c3d3afdee8ee360068e0d331145c9b8055006f8a5ee71682aeb","observation_id":"e4b01d0f-bb0d-4e67-a87d-e5ac9e9876ec","resolution":{"observed_at":"2026-08-12T12:27:30.290453Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.275288Z","title":null,"venue":null,"work_id":"ac5b1c3b-f8d0-4634-a28d-ee233f8b4bde","year":2006},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.752149Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:f46e224a4f9dda0366b3bc32c561a2531f83b7fa1ae365b174b5c36b19ee97a7","observation_id":"33adadb8-184a-4903-bd98-b1409ef8e3dc","resolution":{"observed_at":"2026-08-12T12:27:30.279821Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.264967Z","title":null,"venue":null,"work_id":"16031593-2568-4dd9-9a97-d87654c2ca3d","year":2020},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.755794Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:47962b7b67bdd783ee7c1acdba35c01698cc19f4ef900537b83afbb746e6f4b3","observation_id":"180f74d9-aa74-4b29-8d69-1cd40c5149f8","resolution":{"observed_at":"2026-08-12T12:27:30.268408Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.254183Z","title":null,"venue":null,"work_id":"4825b755-85fb-4995-adc2-66380067a61f","year":2018},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.759868Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:c96ead1801c267bad61d7d4e01e792aabc592e5e641b40c21f590f5e8a791cb7","observation_id":"f23cad65-3003-4e20-9dca-86025911a9ef","resolution":{"observed_at":"2026-08-12T12:27:30.257776Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.243352Z","title":"Keogh, J","venue":null,"work_id":"16db52be-e1af-4aff-9c1a-9a76fa0c57ce","year":2005},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.763419Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:d9daed4cb6d3817f92fd0dc6b927a2c1af8894507e1a3ec186efe6e7001cf45d","observation_id":"6f206791-4dc3-44a8-9532-0ae7250e1625","resolution":{"observed_at":"2026-08-12T12:27:30.247313Z","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":"2026-08-12T12:27:30.232630Z","title":null,"venue":null,"work_id":"34a881a3-5d9f-47a2-8e45-53c7027abe33","year":2020},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.766943Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:2c41042e03c89fc9262cd42b9a8563c41bf7ca5b0164d83f200ccdb84677cadc","observation_id":"249378da-edd9-4451-9f8e-20d220a385f8","resolution":{"observed_at":"2026-08-12T12:27:30.236560Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.220024Z","title":"Zuluaga, and Eamonn Keogh","venue":null,"work_id":"eb71f89d-850f-4401-82a7-eee60ec11003","year":null},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.771409Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:92fe969c4a73e03eb12f99ed22c05d4a793814fd139cb88d8fc658676c094a2a","observation_id":"2098d103-53f5-4368-92bb-7059fd4e5550","resolution":{"observed_at":"2026-08-12T12:27:30.224626Z","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":"2026-08-12T12:27:30.195262Z","title":null,"venue":null,"work_id":"1526b5e6-0c83-4e11-9428-e4bc113f1925","year":2003},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.780667Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:d61ca50c65ce42685b7bf0a91299a1c39df5bf7c6601701147513c9b9b66b354","observation_id":"8106614b-9ea6-495c-8cd7-bd7ccd14da81","resolution":{"observed_at":"2026-08-12T12:27:30.199330Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.183654Z","title":null,"venue":null,"work_id":"62cbc218-2fd4-41d3-ab46-0d93b2cf21da","year":2015},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.784768Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:7e3e969afcab747453be961520e764f6ccb60648835228aa86a4a396c687f10e","observation_id":"618e9b52-b23e-4150-b0de-2362393ad73e","resolution":{"observed_at":"2026-08-12T12:27:30.187601Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.207928Z","title":"1173–1182","venue":null,"work_id":"164382cd-3229-452d-a567-5475bc03cf03","year":null},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.776379Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:bb7434fca422ac3b6fd15f369b17dcbaf604f98f78340406f9a4023e82650ec5","observation_id":"abf7a574-0898-4d87-9d34-888e9362bac4","resolution":{"observed_at":"2026-08-12T12:27:30.212030Z","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":"2026-08-12T12:27:30.159890Z","title":null,"venue":null,"work_id":"990c93f3-1248-4721-8c06-d65c20c5a67f","year":2020},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.792315Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:32acc59af1d754f4ee2b17e60151498ce508ef50a5f32cb26cf7c65de884761a","observation_id":"b54105f8-6b07-4790-8a4d-47ec2191033b","resolution":{"observed_at":"2026-08-12T12:27:30.163732Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.796149Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.796149Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:d03d68b51529d6e0011d17c353c328c9984cb71e11861e363f6be15b69456227","observation_id":"b4833069-0f3b-44c0-92b5-bb60252ee34c","resolution":{"observed_at":"2026-08-12T12:27:29.796149Z","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":"2026-08-12T12:27:30.171505Z","title":null,"venue":null,"work_id":"df2c0f86-f2d3-44a4-82f9-226f5aed149f","year":2001},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.788535Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:3d5cb52b0ef7dbfbb1e145e52cacad74dcb5f8a03a20bf4fdae72c1778e9859c","observation_id":"71239b7b-6bc5-43e2-a46f-e167775f0e5f","resolution":{"observed_at":"2026-08-12T12:27:30.175425Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.803476Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.803476Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:7f7400ff4dd7f13f95e144954120b3f7765584016919e35634e3a24e61acc7b1","observation_id":"37aa787c-51d5-412a-ad7c-061b860ad756","resolution":{"observed_at":"2026-08-12T12:27:29.803476Z","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":"2026-08-12T12:27:30.120314Z","title":null,"venue":null,"work_id":"10c989f8-71e6-4048-9053-ab808fa99e37","year":2018},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.807138Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:7f73754c3528b6d58cba30d0bdbab17a8726e1102d02f22443e2b64ca5c4ac82","observation_id":"555ff17c-d4f0-4358-8d25-af8021e418c3","resolution":{"observed_at":"2026-08-12T12:27:30.124414Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.139674Z","title":null,"venue":null,"work_id":"68d29369-b7df-4ef6-a421-235d82076677","year":2022},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.799397Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:8941eb0cf78834bed5cccd2558711383cab7d356e800857deb2b606019200bc3","observation_id":"336d5bdd-f6db-48f0-8283-94a9a69635e8","resolution":{"observed_at":"2026-08-12T12:27:30.144662Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.815112Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.815112Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:2bd5b34bd56f3d34b467e47c1288568b02bab76c3ece1563d567cd7a55b15f6f","observation_id":"3bd546f9-404f-4b58-8596-55df37ae1f31","resolution":{"observed_at":"2026-08-12T12:27:29.815112Z","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":"2026-08-12T12:27:30.099656Z","title":null,"venue":null,"work_id":"29b8ec6a-7910-44fe-8479-8b9f380d18f8","year":2017},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.818832Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:c576fc54196941fe04ea40839fe894ac5707cdda3b05d723a0818c27ab358661","observation_id":"4284ccd8-6d1c-4a34-91d2-b739fbd7c8d5","resolution":{"observed_at":"2026-08-12T12:27:30.103258Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2006.00339","last_updated":"2023-01-27T06:46:07Z","snapshot_observed_at":"2026-08-13T22:34:10.030082Z","submitted_at":"2020-05-30T19:30:38Z","title":"Rethinking Assumptions in Deep Anomaly Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.00339","snapshot_observed_at":"2026-08-12T12:27:29.810652Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.810652Z"},"links":{"cited_paper":"/paper/2006.00339","citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:3e93fcf0d8d280f57a05cb59059da2585d757eea377464ad02bed405df83dcaf","observation_id":"92149433-2b8b-47ab-98cb-3882a2916446","resolution":{"observed_at":"2026-08-12T12:27:29.810652Z","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":"2026-08-12T12:27:30.080771Z","title":null,"venue":null,"work_id":"79fb00e3-649f-45b5-9b66-509b7acf0174","year":2020},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.827474Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:37e0629cfbc43e41b14019fbc902de07de47706559af3b7ade8269c856aa0150","observation_id":"e42dec6a-13d6-4904-ba20-25f6cdbdb6db","resolution":{"observed_at":"2026-08-12T12:27:30.084568Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.069260Z","title":null,"venue":null,"work_id":"735fbb8b-3754-4ace-be0e-cc54dae069a9","year":2019},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.831185Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:b22e26c2cfaa8515d19a2bed4d82c327b9befcf83787dbd69119c1419d081c87","observation_id":"de9c5915-7649-456f-9bdb-3cff60170fb3","resolution":{"observed_at":"2026-08-12T12:27:30.073360Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.823760Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.823760Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:7b081a32f42bba7c462d2e928ffd34e83e3dca37238134cac56f2b08dbe680aa","observation_id":"fb6d41a3-1aba-4ef2-a900-5dd3fbc22135","resolution":{"observed_at":"2026-08-12T12:27:29.823760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:29.838441Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.838441Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:74d4e0f7eceae2eed37ec3dde87fc037d3ec91a22fb3e0af456619245e4f0e68","observation_id":"f59061a6-42bf-414a-85da-0191a9abb07a","resolution":{"observed_at":"2026-08-12T12:27:29.838441Z","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":"2026-08-12T12:27:30.042099Z","title":null,"venue":null,"work_id":"de7e4588-17a4-4f45-8f39-e1dd1f10d5c0","year":2022},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.841974Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:b14a6a5341b20110310ab0af29286ceb64f7d1f2e582b2d56fd51d606dc7d28e","observation_id":"e88b03b2-e590-4b81-8c34-c6c031a506d6","resolution":{"observed_at":"2026-08-12T12:27:30.046132Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.834648Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.834648Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:c53bc7a9fbcdd3e055124e8b0628157d18581ed1ff743b97aaafa6f2cf1a2f91","observation_id":"53a7d37f-edae-44eb-a110-051a6c6f1cf4","resolution":{"observed_at":"2026-08-12T12:27:29.834648Z","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":"2026-08-12T12:27:30.013572Z","title":null,"venue":null,"work_id":"488aa09f-7e99-4b9f-9dfe-52a81a278105","year":2021},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.850012Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:dd3914b566953430aeb60bf45155f2ad96a507c7f554afda09eadf7885a4f36f","observation_id":"cbf40bcd-0a20-4ba5-b749-f87e5183ff57","resolution":{"observed_at":"2026-08-12T12:27:30.018152Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.000837Z","title":null,"venue":null,"work_id":"511c0fec-a37f-4697-b1fb-2da389c2aead","year":2007},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.853468Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:503962541b06275026f07fda71eb92a6db1f95a5a9a178ca032601f77f26609d","observation_id":"4cdfce64-66ca-4f4f-9842-6a20fd863912","resolution":{"observed_at":"2026-08-12T12:27:30.004795Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.028079Z","title":null,"venue":null,"work_id":"215440ec-36d6-446d-8eae-63174e44e7af","year":2021},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.845760Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:73ae0b7c83e1479505aa3ce1d9cd99c0fde826ad4a4ec17e0f5ead7acbb5afba","observation_id":"de9497a8-890a-4ec4-a85e-dd01ceef5679","resolution":{"observed_at":"2026-08-12T12:27:30.033536Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.975824Z","title":null,"venue":null,"work_id":"6ac78655-9bb2-4061-a04f-9054823bb351","year":2016},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.860823Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:4d748f8afb0d9492e3a48b9242f24eaeddcaec4821902ea05fdcf303fdc9c338","observation_id":"b1bce879-e6c0-46cb-9292-01772687a7bf","resolution":{"observed_at":"2026-08-12T12:27:29.979471Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.963917Z","title":null,"venue":null,"work_id":"857aeeaf-1d40-4903-85d2-2da0ffe38b8c","year":2019},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.865093Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:10c38655765ee0aa4de05d8f509ec7fbc0caac8213f237ffbed19d54f1bf5798","observation_id":"3db8edb4-40b5-45f0-b520-d3e098884c8b","resolution":{"observed_at":"2026-08-12T12:27:29.967872Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.988456Z","title":null,"venue":null,"work_id":"e0b8fb46-aa7c-4e3c-9028-fd2787ea239c","year":2008},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.856972Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:75d3614771cc1af3d30eface66639e8a96130a672c688775ecf5dc0d0afc02b2","observation_id":"34d5b73b-6f78-4b98-8cb4-d580eb9866a2","resolution":{"observed_at":"2026-08-12T12:27:29.992501Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.939200Z","title":null,"venue":null,"work_id":"5abef3a0-a088-4764-b1ac-d772f1c16312","year":2020},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.873088Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:a85a35bbfc7f01d99738ac81280bdec4454e86f13f3c7104a3359f5b81b33f93","observation_id":"4bf3fd74-d3f8-4c5b-bd42-93b49c349456","resolution":{"observed_at":"2026-08-12T12:27:29.943023Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.927058Z","title":null,"venue":null,"work_id":"7a9f5ebc-845b-4853-8fa4-c93a89e3e384","year":2018},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.877071Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:93efc8b044f49f0144134254618ae69bc332253dbf036b2d0bfa9a01bb238adc","observation_id":"da1e982a-1808-48ae-a688-862a19807add","resolution":{"observed_at":"2026-08-12T12:27:29.931589Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:29.951420Z","title":null,"venue":null,"work_id":"1394062c-4e28-4969-9e2c-a5508c601f5d","year":2021},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.869717Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:7377dfeaf0d1ba37359bca9c30ab51f69074d6d7139f8cb2542b48d73e60bb9a","observation_id":"4c45ee2c-9abf-430b-ba49-a24e26eced1a","resolution":{"observed_at":"2026-08-12T12:27:29.955191Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:27:30.304893Z","title":"IEEE Internet of Things Journal 9, 12 (2021), 9179–9189","venue":null,"work_id":"42b6547f-624d-4173-8534-f38dea6a9a1c","year":2021},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.740489Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:7262f16e479c7f91a74bbc544d1fb15d83ae6222c807ded1d3a6bdd6a60a0d60","observation_id":"797dc8f3-1c3a-4c0b-ae57-a38859faf26c","resolution":{"observed_at":"2026-08-12T12:27:30.309461Z","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":"2026-08-12T12:27:30.335941Z","title":"In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI)","venue":null,"work_id":"753780e4-1549-4cb5-a364-af41d9d0c192","year":null},"citing_paper":{"arxiv_id":"2411.17218","last_updated":"2024-11-26T08:36:07Z","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:29.728541Z"},"links":{"citing_paper":"/paper/2411.17218"},"observation_digest":"sha256:a6f78388563e1917f8578e6f3cda379610671f63927b8e4f30bdf25429cfc7c6","observation_id":"b539d5ba-8b52-4eb8-9847-2e9bda717574","resolution":{"observed_at":"2026-08-12T12:27:30.340968Z","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":"2411.17218","last_updated":"2024-11-26T08:36:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T14:37:48.032768Z","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":40,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":45},"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 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2411.17218."}