{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NFBWYB2NX4KKOOZPLN2Q5WZX7G","short_pith_number":"pith:NFBWYB2N","schema_version":"1.0","canonical_sha256":"69436c074dbf14a73b2f5b750edb37f9999018673b622ad4e4306bce46bf8f3f","source":{"kind":"arxiv","id":"2211.00082","version":1},"attestation_state":"computed","paper":{"title":"Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ming Dong, Soumyanil Banerjee, Weisong Shi","submitted_at":"2022-10-31T18:29:40Z","abstract_excerpt":"COVID-19 has become a matter of serious concern over the last few years. It has adversely affected numerous people around the globe and has led to the loss of billions of dollars of business capital. In this paper, we propose a novel Spatial-Temporal Synchronous Graph Transformer network (STSGT) to capture the complex spatial and temporal dependency of the COVID-19 time series data and forecast the future status of an evolving pandemic. The layers of STSGT combine the graph convolution network (GCN) with the self-attention mechanism of transformers on a synchronous spatial-temporal graph to ca"},"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":"2211.00082","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-31T18:29:40Z","cross_cats_sorted":[],"title_canon_sha256":"a9304e30f47efeb6a937236a32e3c3eefbcc3a89e1b1b95c0951da212bf1a239","abstract_canon_sha256":"1ea42ba385b48ad3f927ef9755fdc9b9ba55d0827581670ea0ae3d8f963456ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:18.219778Z","signature_b64":"klNTsgtoskquzMWsSxoCnfcAQzkjdG4d4ko9zij2Vp3p+GLyNQLUfnQOkkCuv9OCC8r4zfvwcEam4k6OfNHSBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69436c074dbf14a73b2f5b750edb37f9999018673b622ad4e4306bce46bf8f3f","last_reissued_at":"2026-07-05T05:12:18.219296Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:18.219296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ming Dong, Soumyanil Banerjee, Weisong Shi","submitted_at":"2022-10-31T18:29:40Z","abstract_excerpt":"COVID-19 has become a matter of serious concern over the last few years. It has adversely affected numerous people around the globe and has led to the loss of billions of dollars of business capital. In this paper, we propose a novel Spatial-Temporal Synchronous Graph Transformer network (STSGT) to capture the complex spatial and temporal dependency of the COVID-19 time series data and forecast the future status of an evolving pandemic. The layers of STSGT combine the graph convolution network (GCN) with the self-attention mechanism of transformers on a synchronous spatial-temporal graph to ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00082","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/2211.00082/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":"2211.00082","created_at":"2026-07-05T05:12:18.219363+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.00082v1","created_at":"2026-07-05T05:12:18.219363+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00082","created_at":"2026-07-05T05:12:18.219363+00:00"},{"alias_kind":"pith_short_12","alias_value":"NFBWYB2NX4KK","created_at":"2026-07-05T05:12:18.219363+00:00"},{"alias_kind":"pith_short_16","alias_value":"NFBWYB2NX4KKOOZP","created_at":"2026-07-05T05:12:18.219363+00:00"},{"alias_kind":"pith_short_8","alias_value":"NFBWYB2N","created_at":"2026-07-05T05:12:18.219363+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/NFBWYB2NX4KKOOZPLN2Q5WZX7G","json":"https://pith.science/pith/NFBWYB2NX4KKOOZPLN2Q5WZX7G.json","graph_json":"https://pith.science/api/pith-number/NFBWYB2NX4KKOOZPLN2Q5WZX7G/graph.json","events_json":"https://pith.science/api/pith-number/NFBWYB2NX4KKOOZPLN2Q5WZX7G/events.json","paper":"https://pith.science/paper/NFBWYB2N"},"agent_actions":{"view_html":"https://pith.science/pith/NFBWYB2NX4KKOOZPLN2Q5WZX7G","download_json":"https://pith.science/pith/NFBWYB2NX4KKOOZPLN2Q5WZX7G.json","view_paper":"https://pith.science/paper/NFBWYB2N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.00082&json=true","fetch_graph":"https://pith.science/api/pith-number/NFBWYB2NX4KKOOZPLN2Q5WZX7G/graph.json","fetch_events":"https://pith.science/api/pith-number/NFBWYB2NX4KKOOZPLN2Q5WZX7G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NFBWYB2NX4KKOOZPLN2Q5WZX7G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NFBWYB2NX4KKOOZPLN2Q5WZX7G/action/storage_attestation","attest_author":"https://pith.science/pith/NFBWYB2NX4KKOOZPLN2Q5WZX7G/action/author_attestation","sign_citation":"https://pith.science/pith/NFBWYB2NX4KKOOZPLN2Q5WZX7G/action/citation_signature","submit_replication":"https://pith.science/pith/NFBWYB2NX4KKOOZPLN2Q5WZX7G/action/replication_record"}},"created_at":"2026-07-05T05:12:18.219363+00:00","updated_at":"2026-07-05T05:12:18.219363+00:00"}