{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JJRU5MQD5QVYGNWN3FRO3XEJDV","short_pith_number":"pith:JJRU5MQD","schema_version":"1.0","canonical_sha256":"4a634eb203ec2b8336cdd962eddc891d5df6752f97b93cf042694db7724cdb86","source":{"kind":"arxiv","id":"2606.30399","version":1},"attestation_state":"computed","paper":{"title":"Multiscale Dynamic Dependence Estimation over Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Cristian F. Jim\\'enez-Var\\'on, Marina I. Knight, Matthew A. Nunes","submitted_at":"2026-06-29T14:47:08Z","abstract_excerpt":"In numerous scientific and industrial settings, observed multivariate time series are often nonstationary in nature, i.e., comprise data whose second order properties vary over time. An additional feature of many modern datasets is that the cross-dependencies of such series are structured by an underlying network, giving rise to complex interactions between temporal dynamics and network topology. In this article we propose Locally Stationary Wavelet processes on Networks (Net-LSW), a new framework for modelling multiscale, time-varying dependencies that explicitly incorporates the network stru"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.30399","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-06-29T14:47:08Z","cross_cats_sorted":[],"title_canon_sha256":"ef7ae1ffdd65ca168020814574b06b1271ba97bfd61524324e09b6df97c85718","abstract_canon_sha256":"bce1d84dff02fa1ebbc83fcb1fcaf09b87a1a45f32204314b7544a5488f00537"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:18:13.708979Z","signature_b64":"bHnbnoHe6BFihmQJsLbjY4Wqm9MdwU7YxhdD/2CPbcIfdFMn8KBvbOCv3KEhjhPOBVMOW0imQRxIILfdZsb6CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a634eb203ec2b8336cdd962eddc891d5df6752f97b93cf042694db7724cdb86","last_reissued_at":"2026-06-30T02:18:13.708520Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:18:13.708520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multiscale Dynamic Dependence Estimation over Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Cristian F. Jim\\'enez-Var\\'on, Marina I. Knight, Matthew A. Nunes","submitted_at":"2026-06-29T14:47:08Z","abstract_excerpt":"In numerous scientific and industrial settings, observed multivariate time series are often nonstationary in nature, i.e., comprise data whose second order properties vary over time. An additional feature of many modern datasets is that the cross-dependencies of such series are structured by an underlying network, giving rise to complex interactions between temporal dynamics and network topology. In this article we propose Locally Stationary Wavelet processes on Networks (Net-LSW), a new framework for modelling multiscale, time-varying dependencies that explicitly incorporates the network stru"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30399","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/2606.30399/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.30399","created_at":"2026-06-30T02:18:13.708586+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30399v1","created_at":"2026-06-30T02:18:13.708586+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30399","created_at":"2026-06-30T02:18:13.708586+00:00"},{"alias_kind":"pith_short_12","alias_value":"JJRU5MQD5QVY","created_at":"2026-06-30T02:18:13.708586+00:00"},{"alias_kind":"pith_short_16","alias_value":"JJRU5MQD5QVYGNWN","created_at":"2026-06-30T02:18:13.708586+00:00"},{"alias_kind":"pith_short_8","alias_value":"JJRU5MQD","created_at":"2026-06-30T02:18:13.708586+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV","json":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV.json","graph_json":"https://pith.science/api/pith-number/JJRU5MQD5QVYGNWN3FRO3XEJDV/graph.json","events_json":"https://pith.science/api/pith-number/JJRU5MQD5QVYGNWN3FRO3XEJDV/events.json","paper":"https://pith.science/paper/JJRU5MQD"},"agent_actions":{"view_html":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV","download_json":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV.json","view_paper":"https://pith.science/paper/JJRU5MQD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30399&json=true","fetch_graph":"https://pith.science/api/pith-number/JJRU5MQD5QVYGNWN3FRO3XEJDV/graph.json","fetch_events":"https://pith.science/api/pith-number/JJRU5MQD5QVYGNWN3FRO3XEJDV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV/action/storage_attestation","attest_author":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV/action/author_attestation","sign_citation":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV/action/citation_signature","submit_replication":"https://pith.science/pith/JJRU5MQD5QVYGNWN3FRO3XEJDV/action/replication_record"}},"created_at":"2026-06-30T02:18:13.708586+00:00","updated_at":"2026-06-30T02:18:13.708586+00:00"}