{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MKCUXNLD7FIWB6QK3GBCMNCFEP","short_pith_number":"pith:MKCUXNLD","canonical_record":{"source":{"id":"2411.16972","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T22:49:01Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"74f4ce16318e95a786e85a5605828f47d55096ddc21cd852f1bd47e14187cf9a","abstract_canon_sha256":"68e26ac3b6534fef0f3d0631487d305b6f1e1b36ab4f55c83edfddd7c4eb1ac2"},"schema_version":"1.0"},"canonical_sha256":"62854bb563f95160fa0ad98226344523e3cc456551be51c52026de192a4e5afe","source":{"kind":"arxiv","id":"2411.16972","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.16972","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"arxiv_version","alias_value":"2411.16972v1","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16972","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"pith_short_12","alias_value":"MKCUXNLD7FIW","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"pith_short_16","alias_value":"MKCUXNLD7FIWB6QK","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"pith_short_8","alias_value":"MKCUXNLD","created_at":"2026-07-05T09:40:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MKCUXNLD7FIWB6QK3GBCMNCFEP","target":"record","payload":{"canonical_record":{"source":{"id":"2411.16972","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T22:49:01Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"74f4ce16318e95a786e85a5605828f47d55096ddc21cd852f1bd47e14187cf9a","abstract_canon_sha256":"68e26ac3b6534fef0f3d0631487d305b6f1e1b36ab4f55c83edfddd7c4eb1ac2"},"schema_version":"1.0"},"canonical_sha256":"62854bb563f95160fa0ad98226344523e3cc456551be51c52026de192a4e5afe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:25.395639Z","signature_b64":"ZeCf8kDtwERJTcldisz64qDPCQcilApTKq078LOT3c+DOGkmb7p929RmtjNrNsQiiOzPZmuB4NpUHMEvj/mLCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62854bb563f95160fa0ad98226344523e3cc456551be51c52026de192a4e5afe","last_reissued_at":"2026-07-05T09:40:25.395173Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:25.395173Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.16972","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":"2026-07-05T09:40:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XWJZOOaaZ+J4dejAuE6bStFgCEOgbl6DXtuW0zbsJitbNUhs0dQ3lREwxofSuVvk6L3k9d1k3jr0LiE1ARFqDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T10:28:30.230122Z"},"content_sha256":"d4276d68a26b6ca5d1f27efb4bff59304c06fb259641de5ef2568264cc017f55","schema_version":"1.0","event_id":"sha256:d4276d68a26b6ca5d1f27efb4bff59304c06fb259641de5ef2568264cc017f55"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MKCUXNLD7FIWB6QK3GBCMNCFEP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Amirabbas Afzali, Arash Amini, Hesam Hosseini, Mohmmadamin Mirzai","submitted_at":"2024-11-25T22:49:01Z","abstract_excerpt":"Time series data analysis is prevalent across various domains, including finance, healthcare, and environmental monitoring. Traditional time series clustering methods often struggle to capture the complex temporal dependencies inherent in such data. In this paper, we propose the Variational Mixture Graph Autoencoder (VMGAE), a graph-based approach for time series clustering that leverages the structural advantages of graphs to capture enriched data relationships and produces Gaussian mixture embeddings for improved separability. Comparisons with baseline methods are included with experimental "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16972","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/2411.16972/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":"2026-07-05T09:40:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9J+258iEI2ECkmKEfWOIPyqE4QHjBuaLebOf0aw0hnwV8/OfW1zA70En/RCZNUczz34arw33xag0IVGW22ubDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T10:28:30.230508Z"},"content_sha256":"8e69c008f196f890adb32abe2819b515e090134396488607290a879581c2ce85","schema_version":"1.0","event_id":"sha256:8e69c008f196f890adb32abe2819b515e090134396488607290a879581c2ce85"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MKCUXNLD7FIWB6QK3GBCMNCFEP/bundle.json","state_url":"https://pith.science/pith/MKCUXNLD7FIWB6QK3GBCMNCFEP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MKCUXNLD7FIWB6QK3GBCMNCFEP/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":"2026-07-07T10:28:30Z","links":{"resolver":"https://pith.science/pith/MKCUXNLD7FIWB6QK3GBCMNCFEP","bundle":"https://pith.science/pith/MKCUXNLD7FIWB6QK3GBCMNCFEP/bundle.json","state":"https://pith.science/pith/MKCUXNLD7FIWB6QK3GBCMNCFEP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MKCUXNLD7FIWB6QK3GBCMNCFEP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MKCUXNLD7FIWB6QK3GBCMNCFEP","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":"68e26ac3b6534fef0f3d0631487d305b6f1e1b36ab4f55c83edfddd7c4eb1ac2","cross_cats_sorted":["cs.AI","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T22:49:01Z","title_canon_sha256":"74f4ce16318e95a786e85a5605828f47d55096ddc21cd852f1bd47e14187cf9a"},"schema_version":"1.0","source":{"id":"2411.16972","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.16972","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"arxiv_version","alias_value":"2411.16972v1","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16972","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"pith_short_12","alias_value":"MKCUXNLD7FIW","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"pith_short_16","alias_value":"MKCUXNLD7FIWB6QK","created_at":"2026-07-05T09:40:25Z"},{"alias_kind":"pith_short_8","alias_value":"MKCUXNLD","created_at":"2026-07-05T09:40:25Z"}],"graph_snapshots":[{"event_id":"sha256:8e69c008f196f890adb32abe2819b515e090134396488607290a879581c2ce85","target":"graph","created_at":"2026-07-05T09:40:25Z","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/2411.16972/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series data analysis is prevalent across various domains, including finance, healthcare, and environmental monitoring. Traditional time series clustering methods often struggle to capture the complex temporal dependencies inherent in such data. In this paper, we propose the Variational Mixture Graph Autoencoder (VMGAE), a graph-based approach for time series clustering that leverages the structural advantages of graphs to capture enriched data relationships and produces Gaussian mixture embeddings for improved separability. Comparisons with baseline methods are included with experimental ","authors_text":"Amirabbas Afzali, Arash Amini, Hesam Hosseini, Mohmmadamin Mirzai","cross_cats":["cs.AI","eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T22:49:01Z","title":"Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16972","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:d4276d68a26b6ca5d1f27efb4bff59304c06fb259641de5ef2568264cc017f55","target":"record","created_at":"2026-07-05T09:40:25Z","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":"68e26ac3b6534fef0f3d0631487d305b6f1e1b36ab4f55c83edfddd7c4eb1ac2","cross_cats_sorted":["cs.AI","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T22:49:01Z","title_canon_sha256":"74f4ce16318e95a786e85a5605828f47d55096ddc21cd852f1bd47e14187cf9a"},"schema_version":"1.0","source":{"id":"2411.16972","kind":"arxiv","version":1}},"canonical_sha256":"62854bb563f95160fa0ad98226344523e3cc456551be51c52026de192a4e5afe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"62854bb563f95160fa0ad98226344523e3cc456551be51c52026de192a4e5afe","first_computed_at":"2026-07-05T09:40:25.395173Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:40:25.395173Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZeCf8kDtwERJTcldisz64qDPCQcilApTKq078LOT3c+DOGkmb7p929RmtjNrNsQiiOzPZmuB4NpUHMEvj/mLCA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:40:25.395639Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.16972","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4276d68a26b6ca5d1f27efb4bff59304c06fb259641de5ef2568264cc017f55","sha256:8e69c008f196f890adb32abe2819b515e090134396488607290a879581c2ce85"],"state_sha256":"c2881c88f25293e0a9fe8e89de4050cc033b828e6da4603127bafda8e8d21911"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oefm3pk8JsKDDqK5j01j3oSTNLY1SPXgC/gWvEQZ/V5ID/x/Q4F0yJGy4ve2QP/+0yvp0TO6cB8hwJcViMirCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T10:28:30.232507Z","bundle_sha256":"978c48bd80206899c47eeff1777af837203b7a0b110716e8729566c811936225"}}