{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZHIF3JYGMH2VPP22KPNZ2L2BM2","short_pith_number":"pith:ZHIF3JYG","schema_version":"1.0","canonical_sha256":"c9d05da70661f557bf5a53db9d2f416694fd91baa08642be48b462596080bc09","source":{"kind":"arxiv","id":"2412.17524","version":1},"attestation_state":"computed","paper":{"title":"STAHGNet: Modeling Hybrid-grained Heterogenous Dependency Efficiently for Traffic Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dengbo He, Jiyao Wang, Lei Chen, Yijia Zhang, Zehua Peng","submitted_at":"2024-12-23T12:48:10Z","abstract_excerpt":"Traffic flow prediction plays a critical role in the intelligent transportation system, and it is also a challenging task because of the underlying complex Spatio-temporal patterns and heterogeneities evolving across time. However, most present works mostly concentrate on solely capturing Spatial-temporal dependency or extracting implicit similarity graphs, but the hybrid-granularity evolution is ignored in their modeling process. In this paper, we proposed a novel data-driven end-to-end framework, named Spatio-Temporal Aware Hybrid Graph Network (STAHGNet), to couple the hybrid-grained hetero"},"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":"2412.17524","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-12-23T12:48:10Z","cross_cats_sorted":[],"title_canon_sha256":"ae02e4861001e43383b3e8e9c40bf0b881137caacf230a292b68109b22f9185d","abstract_canon_sha256":"50787beddeecfe01f24bdf64f93bcef65268decade708ecac49a83722f3f78fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:23.670870Z","signature_b64":"RX83erOW8rYDuk+zQ3Y/g48Vrci+vxMP7PG5Td6eswla1DSFQ0XhVslH8HVjqdkBlXZ3Q9uByjmdTWe0wTpjDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9d05da70661f557bf5a53db9d2f416694fd91baa08642be48b462596080bc09","last_reissued_at":"2026-07-05T09:53:23.670340Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:23.670340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"STAHGNet: Modeling Hybrid-grained Heterogenous Dependency Efficiently for Traffic Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dengbo He, Jiyao Wang, Lei Chen, Yijia Zhang, Zehua Peng","submitted_at":"2024-12-23T12:48:10Z","abstract_excerpt":"Traffic flow prediction plays a critical role in the intelligent transportation system, and it is also a challenging task because of the underlying complex Spatio-temporal patterns and heterogeneities evolving across time. However, most present works mostly concentrate on solely capturing Spatial-temporal dependency or extracting implicit similarity graphs, but the hybrid-granularity evolution is ignored in their modeling process. In this paper, we proposed a novel data-driven end-to-end framework, named Spatio-Temporal Aware Hybrid Graph Network (STAHGNet), to couple the hybrid-grained hetero"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17524","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/2412.17524/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":"2412.17524","created_at":"2026-07-05T09:53:23.670415+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.17524v1","created_at":"2026-07-05T09:53:23.670415+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17524","created_at":"2026-07-05T09:53:23.670415+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZHIF3JYGMH2V","created_at":"2026-07-05T09:53:23.670415+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZHIF3JYGMH2VPP22","created_at":"2026-07-05T09:53:23.670415+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZHIF3JYG","created_at":"2026-07-05T09:53:23.670415+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/ZHIF3JYGMH2VPP22KPNZ2L2BM2","json":"https://pith.science/pith/ZHIF3JYGMH2VPP22KPNZ2L2BM2.json","graph_json":"https://pith.science/api/pith-number/ZHIF3JYGMH2VPP22KPNZ2L2BM2/graph.json","events_json":"https://pith.science/api/pith-number/ZHIF3JYGMH2VPP22KPNZ2L2BM2/events.json","paper":"https://pith.science/paper/ZHIF3JYG"},"agent_actions":{"view_html":"https://pith.science/pith/ZHIF3JYGMH2VPP22KPNZ2L2BM2","download_json":"https://pith.science/pith/ZHIF3JYGMH2VPP22KPNZ2L2BM2.json","view_paper":"https://pith.science/paper/ZHIF3JYG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.17524&json=true","fetch_graph":"https://pith.science/api/pith-number/ZHIF3JYGMH2VPP22KPNZ2L2BM2/graph.json","fetch_events":"https://pith.science/api/pith-number/ZHIF3JYGMH2VPP22KPNZ2L2BM2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZHIF3JYGMH2VPP22KPNZ2L2BM2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZHIF3JYGMH2VPP22KPNZ2L2BM2/action/storage_attestation","attest_author":"https://pith.science/pith/ZHIF3JYGMH2VPP22KPNZ2L2BM2/action/author_attestation","sign_citation":"https://pith.science/pith/ZHIF3JYGMH2VPP22KPNZ2L2BM2/action/citation_signature","submit_replication":"https://pith.science/pith/ZHIF3JYGMH2VPP22KPNZ2L2BM2/action/replication_record"}},"created_at":"2026-07-05T09:53:23.670415+00:00","updated_at":"2026-07-05T09:53:23.670415+00:00"}