{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:6PSNSVYFJUGQOVMYDGATCD7B5C","short_pith_number":"pith:6PSNSVYF","canonical_record":{"source":{"id":"2412.18535","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-24T16:34:50Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"940796348621e17ed8ec579e880c29962f453263b3b1eaa3915fc16067e60662","abstract_canon_sha256":"d265dd424237853edc74f9432a1e4780fc3124472cf7ee8a8f59105b1e8112bb"},"schema_version":"1.0"},"canonical_sha256":"f3e4d957054d0d0755981981310fe1e8b6f8e3d4f539c7d4a27d93aabf0fec1e","source":{"kind":"arxiv","id":"2412.18535","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.18535","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"arxiv_version","alias_value":"2412.18535v2","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.18535","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"pith_short_12","alias_value":"6PSNSVYFJUGQ","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"pith_short_16","alias_value":"6PSNSVYFJUGQOVMY","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"pith_short_8","alias_value":"6PSNSVYF","created_at":"2026-07-05T09:56:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:6PSNSVYFJUGQOVMYDGATCD7B5C","target":"record","payload":{"canonical_record":{"source":{"id":"2412.18535","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-24T16:34:50Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"940796348621e17ed8ec579e880c29962f453263b3b1eaa3915fc16067e60662","abstract_canon_sha256":"d265dd424237853edc74f9432a1e4780fc3124472cf7ee8a8f59105b1e8112bb"},"schema_version":"1.0"},"canonical_sha256":"f3e4d957054d0d0755981981310fe1e8b6f8e3d4f539c7d4a27d93aabf0fec1e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:58.251518Z","signature_b64":"StNUuDOp61hCZHWDm/G/bNBdZyc58AF4jt8+7NfsMLU6+D29RMaZ1RIAlskvUpBSeE7yY1hc4VqO3FqW5V40Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3e4d957054d0d0755981981310fe1e8b6f8e3d4f539c7d4a27d93aabf0fec1e","last_reissued_at":"2026-07-05T09:56:58.251071Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:58.251071Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.18535","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:56:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0mjWYspIDhGXbrGdjhLMjdMz8d1ZjRjvTu3XbC8YVH64u5y5nqJEAdjBVZf9M6xrkl0VWL5lJoAUablow8vsBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T11:32:53.121193Z"},"content_sha256":"e5f77eb39b801115ab35ee3a5d4da5cc544a2f7098c327352952ecc5cfc27df1","schema_version":"1.0","event_id":"sha256:e5f77eb39b801115ab35ee3a5d4da5cc544a2f7098c327352952ecc5cfc27df1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:6PSNSVYFJUGQOVMYDGATCD7B5C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.LG","authors_text":"Xiaojie Yuan, Xinyang Chen, Xinyu Yang, Ying Zhang, Yu Sun","submitted_at":"2024-12-24T16:34:50Z","abstract_excerpt":"Spatial-temporal data collected across different geographic locations often suffer from missing values, posing challenges to data analysis. Existing methods primarily leverage fixed spatial graphs to impute missing values, which implicitly assume that the spatial relationship is roughly the same for all features across different locations. However, they may overlook the different spatial relationships of diverse features recorded by sensors in different locations. To address this, we introduce the multi-scale Graph Structure Learning framework for spatial-temporal Imputation (GSLI) that dynami"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.18535","kind":"arxiv","version":2},"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/2412.18535/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":"2026-07-05T09:56:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ifrIMtG1mVDD5GxqnWuyZuNLJZ/KPdPyppA6yeFziQUUQufnNXYJSLFr9aguCEe6vkVTQ/sxUH/Wl2QbAN6hBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T11:32:53.121586Z"},"content_sha256":"64cf1ccd679ae205e7e2587152477cd5da790af343970f92d4dd67dd34f7886c","schema_version":"1.0","event_id":"sha256:64cf1ccd679ae205e7e2587152477cd5da790af343970f92d4dd67dd34f7886c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6PSNSVYFJUGQOVMYDGATCD7B5C/bundle.json","state_url":"https://pith.science/pith/6PSNSVYFJUGQOVMYDGATCD7B5C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6PSNSVYFJUGQOVMYDGATCD7B5C/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":"2026-07-20T11:32:53Z","links":{"resolver":"https://pith.science/pith/6PSNSVYFJUGQOVMYDGATCD7B5C","bundle":"https://pith.science/pith/6PSNSVYFJUGQOVMYDGATCD7B5C/bundle.json","state":"https://pith.science/pith/6PSNSVYFJUGQOVMYDGATCD7B5C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6PSNSVYFJUGQOVMYDGATCD7B5C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6PSNSVYFJUGQOVMYDGATCD7B5C","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":"d265dd424237853edc74f9432a1e4780fc3124472cf7ee8a8f59105b1e8112bb","cross_cats_sorted":["cs.DB"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-24T16:34:50Z","title_canon_sha256":"940796348621e17ed8ec579e880c29962f453263b3b1eaa3915fc16067e60662"},"schema_version":"1.0","source":{"id":"2412.18535","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.18535","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"arxiv_version","alias_value":"2412.18535v2","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.18535","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"pith_short_12","alias_value":"6PSNSVYFJUGQ","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"pith_short_16","alias_value":"6PSNSVYFJUGQOVMY","created_at":"2026-07-05T09:56:58Z"},{"alias_kind":"pith_short_8","alias_value":"6PSNSVYF","created_at":"2026-07-05T09:56:58Z"}],"graph_snapshots":[{"event_id":"sha256:64cf1ccd679ae205e7e2587152477cd5da790af343970f92d4dd67dd34f7886c","target":"graph","created_at":"2026-07-05T09:56:58Z","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/2412.18535/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatial-temporal data collected across different geographic locations often suffer from missing values, posing challenges to data analysis. Existing methods primarily leverage fixed spatial graphs to impute missing values, which implicitly assume that the spatial relationship is roughly the same for all features across different locations. However, they may overlook the different spatial relationships of diverse features recorded by sensors in different locations. To address this, we introduce the multi-scale Graph Structure Learning framework for spatial-temporal Imputation (GSLI) that dynami","authors_text":"Xiaojie Yuan, Xinyang Chen, Xinyu Yang, Ying Zhang, Yu Sun","cross_cats":["cs.DB"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-24T16:34:50Z","title":"Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.18535","kind":"arxiv","version":2},"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:e5f77eb39b801115ab35ee3a5d4da5cc544a2f7098c327352952ecc5cfc27df1","target":"record","created_at":"2026-07-05T09:56:58Z","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":"d265dd424237853edc74f9432a1e4780fc3124472cf7ee8a8f59105b1e8112bb","cross_cats_sorted":["cs.DB"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-24T16:34:50Z","title_canon_sha256":"940796348621e17ed8ec579e880c29962f453263b3b1eaa3915fc16067e60662"},"schema_version":"1.0","source":{"id":"2412.18535","kind":"arxiv","version":2}},"canonical_sha256":"f3e4d957054d0d0755981981310fe1e8b6f8e3d4f539c7d4a27d93aabf0fec1e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f3e4d957054d0d0755981981310fe1e8b6f8e3d4f539c7d4a27d93aabf0fec1e","first_computed_at":"2026-07-05T09:56:58.251071Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:56:58.251071Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"StNUuDOp61hCZHWDm/G/bNBdZyc58AF4jt8+7NfsMLU6+D29RMaZ1RIAlskvUpBSeE7yY1hc4VqO3FqW5V40Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:56:58.251518Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.18535","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e5f77eb39b801115ab35ee3a5d4da5cc544a2f7098c327352952ecc5cfc27df1","sha256:64cf1ccd679ae205e7e2587152477cd5da790af343970f92d4dd67dd34f7886c"],"state_sha256":"5271ec4b1c69134de8d351923ae4c8633417e4c0239d2d6bcae61f566196bbe2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kXuDeehzhpdJfPYZCruvFSqwhDP7bv9LZvrnEbxc406+Y0zQEMzTGneU4KZwQLTXjNJv+xK8/CDjJq9yT8cODw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-20T11:32:53.124181Z","bundle_sha256":"ecabfcbd86797ce70b856d11c209d7d32a891a9f5a229579ab72a05f99ce6c72"}}