{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:BLUEFRIFG3BVN2XWLA7WGPBH4T","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":"4bb8d43ff2b420e5da1c23101b2b6491f7400ede29e9df776dde6af6fccb116a","cross_cats_sorted":["cs.IR","cs.SI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-15T17:50:37Z","title_canon_sha256":"7284d56db32895cd2b7d68af4fe2944d40ea772ce8a4ffeb8970c7f684dab852"},"schema_version":"1.0","source":{"id":"2108.06783","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.06783","created_at":"2026-07-05T03:05:57Z"},{"alias_kind":"arxiv_version","alias_value":"2108.06783v1","created_at":"2026-07-05T03:05:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.06783","created_at":"2026-07-05T03:05:57Z"},{"alias_kind":"pith_short_12","alias_value":"BLUEFRIFG3BV","created_at":"2026-07-05T03:05:57Z"},{"alias_kind":"pith_short_16","alias_value":"BLUEFRIFG3BVN2XW","created_at":"2026-07-05T03:05:57Z"},{"alias_kind":"pith_short_8","alias_value":"BLUEFRIF","created_at":"2026-07-05T03:05:57Z"}],"graph_snapshots":[{"event_id":"sha256:fcdb493a3bc9b94637d4ecfeecb40b1b697c87dee24f3303fa94eeff1f16994f","target":"graph","created_at":"2026-07-05T03:05:57Z","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/2108.06783/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data. The de-facto solution to model the dependencies is to feed the data into a recurrent neural network (RNN). However, the fully connected network structure underneath the RNN (either GRU or LSTM) assumes a static and complete dependency graph between time-series, which may not hold in many real-world applications. To alleviate this assumption, we propose a dynamic bipartite graph structure to encode the inter-dependencies between time-series. More concret","authors_text":"Fei Wang, Hao Yang, Lan Wang, Mengting Gu, Yuhang Wu, Yusan Lin","cross_cats":["cs.IR","cs.SI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-15T17:50:37Z","title":"Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.06783","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:aa80ab43096cbf5f9e361409f33d74670581973bf1bc5b46a9ab554da7d1038a","target":"record","created_at":"2026-07-05T03:05:57Z","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":"4bb8d43ff2b420e5da1c23101b2b6491f7400ede29e9df776dde6af6fccb116a","cross_cats_sorted":["cs.IR","cs.SI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-15T17:50:37Z","title_canon_sha256":"7284d56db32895cd2b7d68af4fe2944d40ea772ce8a4ffeb8970c7f684dab852"},"schema_version":"1.0","source":{"id":"2108.06783","kind":"arxiv","version":1}},"canonical_sha256":"0ae842c50536c356eaf6583f633c27e4e85880745b8537cfea41e70fb194301b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ae842c50536c356eaf6583f633c27e4e85880745b8537cfea41e70fb194301b","first_computed_at":"2026-07-05T03:05:57.864797Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:05:57.864797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9Ytpp+imLZgKu11gFJq3K3jggtRp3FpWiaYMql7vYV/AsbVOzkIipBGqRs11CghPob045W9R3bySCg+VfWs4Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:05:57.865233Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.06783","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aa80ab43096cbf5f9e361409f33d74670581973bf1bc5b46a9ab554da7d1038a","sha256:fcdb493a3bc9b94637d4ecfeecb40b1b697c87dee24f3303fa94eeff1f16994f"],"state_sha256":"5e4b74cdde3e5cf958eb7d9dd0ba1cfbd1d2c283e8b23e4498254fcff60e7b00"}