{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FN52Q3Z4I7MC42RSMHUO7F6DN7","short_pith_number":"pith:FN52Q3Z4","canonical_record":{"source":{"id":"2411.17218","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-26T08:36:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ce0bdbb433323f06b91644e2f45dd8c7bd69d1c0eb996afebb8891b965bbdcdf","abstract_canon_sha256":"358fc5358682e738f36aab336e207eaf85dab56e930711f641ddab9f1c13556c"},"schema_version":"1.0"},"canonical_sha256":"2b7ba86f3c47d82e6a3261e8ef97c36fd6a042ddc3e8cde3d63a9f5f09561334","source":{"kind":"arxiv","id":"2411.17218","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.17218","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"arxiv_version","alias_value":"2411.17218v1","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17218","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"pith_short_12","alias_value":"FN52Q3Z4I7MC","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"pith_short_16","alias_value":"FN52Q3Z4I7MC42RS","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"pith_short_8","alias_value":"FN52Q3Z4","created_at":"2026-07-05T09:40:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FN52Q3Z4I7MC42RSMHUO7F6DN7","target":"record","payload":{"canonical_record":{"source":{"id":"2411.17218","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-26T08:36:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ce0bdbb433323f06b91644e2f45dd8c7bd69d1c0eb996afebb8891b965bbdcdf","abstract_canon_sha256":"358fc5358682e738f36aab336e207eaf85dab56e930711f641ddab9f1c13556c"},"schema_version":"1.0"},"canonical_sha256":"2b7ba86f3c47d82e6a3261e8ef97c36fd6a042ddc3e8cde3d63a9f5f09561334","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:29.158067Z","signature_b64":"nFmzFG+0lGmQegwnTK2et4scbMCRk/g4HxB69dDuC8ZFg4om8Ew1q3QrQuRdewQh2t5K9aZn6GVKyVVNfCc+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b7ba86f3c47d82e6a3261e8ef97c36fd6a042ddc3e8cde3d63a9f5f09561334","last_reissued_at":"2026-07-05T09:40:29.157582Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:29.157582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.17218","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:40:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jzTOrzkO/hfqcliCjBN0h8Xs2pZBQOdIvvjBqOCLTPSU73FsQC3B+GlswpGyrV33+ooluMcnpvbf5eOTv6llDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T23:45:33.812005Z"},"content_sha256":"fcf7502c359b901f0759783faefd87eed02464512b3c15c798cf71884384d6ab","schema_version":"1.0","event_id":"sha256:fcf7502c359b901f0759783faefd87eed02464512b3c15c798cf71884384d6ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FN52Q3Z4I7MC42RSMHUO7F6DN7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Liang Sun, Qingsong Wen, Weiqi Chen, Zhiqiang Zhou","submitted_at":"2024-11-26T08:36:07Z","abstract_excerpt":"Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to the following reasons: 1) how to effectively learn complex dynamics and dependencies in time series; 2) diverse and complicated anomalous subsequences as well as the inherent variance and noise of normal patterns; 3) how to determine the proper subsequence length for effective detection, which is a required parameter for many existing algorithms. In this paper, we present a novel approach to subsequence anomaly detection"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17218","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.17218/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:40:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vHJNRwxChfohfp/Jl7QAXHbCBfgQVwXh7jrx9rg3xCwOskeqdA5xQZxyaDLg4NOOMafThGb5pB3V6wckPtdiCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T23:45:33.812547Z"},"content_sha256":"03be55a68875933f8344edaf6464a13247ec415bce41f970ac2bd2fb9078e53e","schema_version":"1.0","event_id":"sha256:03be55a68875933f8344edaf6464a13247ec415bce41f970ac2bd2fb9078e53e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FN52Q3Z4I7MC42RSMHUO7F6DN7/bundle.json","state_url":"https://pith.science/pith/FN52Q3Z4I7MC42RSMHUO7F6DN7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FN52Q3Z4I7MC42RSMHUO7F6DN7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T23:45:33Z","links":{"resolver":"https://pith.science/pith/FN52Q3Z4I7MC42RSMHUO7F6DN7","bundle":"https://pith.science/pith/FN52Q3Z4I7MC42RSMHUO7F6DN7/bundle.json","state":"https://pith.science/pith/FN52Q3Z4I7MC42RSMHUO7F6DN7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FN52Q3Z4I7MC42RSMHUO7F6DN7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FN52Q3Z4I7MC42RSMHUO7F6DN7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"358fc5358682e738f36aab336e207eaf85dab56e930711f641ddab9f1c13556c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-26T08:36:07Z","title_canon_sha256":"ce0bdbb433323f06b91644e2f45dd8c7bd69d1c0eb996afebb8891b965bbdcdf"},"schema_version":"1.0","source":{"id":"2411.17218","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.17218","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"arxiv_version","alias_value":"2411.17218v1","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17218","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"pith_short_12","alias_value":"FN52Q3Z4I7MC","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"pith_short_16","alias_value":"FN52Q3Z4I7MC42RS","created_at":"2026-07-05T09:40:29Z"},{"alias_kind":"pith_short_8","alias_value":"FN52Q3Z4","created_at":"2026-07-05T09:40:29Z"}],"graph_snapshots":[{"event_id":"sha256:03be55a68875933f8344edaf6464a13247ec415bce41f970ac2bd2fb9078e53e","target":"graph","created_at":"2026-07-05T09:40:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.17218/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to the following reasons: 1) how to effectively learn complex dynamics and dependencies in time series; 2) diverse and complicated anomalous subsequences as well as the inherent variance and noise of normal patterns; 3) how to determine the proper subsequence length for effective detection, which is a required parameter for many existing algorithms. In this paper, we present a novel approach to subsequence anomaly detection","authors_text":"Liang Sun, Qingsong Wen, Weiqi Chen, Zhiqiang Zhou","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-26T08:36:07Z","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17218","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fcf7502c359b901f0759783faefd87eed02464512b3c15c798cf71884384d6ab","target":"record","created_at":"2026-07-05T09:40:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"358fc5358682e738f36aab336e207eaf85dab56e930711f641ddab9f1c13556c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-26T08:36:07Z","title_canon_sha256":"ce0bdbb433323f06b91644e2f45dd8c7bd69d1c0eb996afebb8891b965bbdcdf"},"schema_version":"1.0","source":{"id":"2411.17218","kind":"arxiv","version":1}},"canonical_sha256":"2b7ba86f3c47d82e6a3261e8ef97c36fd6a042ddc3e8cde3d63a9f5f09561334","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b7ba86f3c47d82e6a3261e8ef97c36fd6a042ddc3e8cde3d63a9f5f09561334","first_computed_at":"2026-07-05T09:40:29.157582Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:40:29.157582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nFmzFG+0lGmQegwnTK2et4scbMCRk/g4HxB69dDuC8ZFg4om8Ew1q3QrQuRdewQh2t5K9aZn6GVKyVVNfCc+DA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:40:29.158067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.17218","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fcf7502c359b901f0759783faefd87eed02464512b3c15c798cf71884384d6ab","sha256:03be55a68875933f8344edaf6464a13247ec415bce41f970ac2bd2fb9078e53e"],"state_sha256":"11ed85701428d837b2fd6197c018eb91c924796ed8d1270539e667831957f987"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xXJTRNhYFDctB+6lbHPh1r4pw1GxlJtpXC+L2S+i3ltIsjJVKaQyj+8SR58bo7GAIbyVdxsDUqj4nfAP305ICA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T23:45:33.820951Z","bundle_sha256":"cd8a44b49c1e0da11d9403402fad90681a52f6efaeda25003c6a06dbbc9571ae"}}