{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4ZEHL66EQVP4X44KMETPSJNKGN","short_pith_number":"pith:4ZEHL66E","canonical_record":{"source":{"id":"2410.00385","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T04:15:48Z","cross_cats_sorted":["cs.AI","cs.DB"],"title_canon_sha256":"5bc697ea8db8dd95322a35810dedda45b29afde487196414fdcaa6a994c03eba","abstract_canon_sha256":"29f90d05e03a8330f0602030271d79dbba035edbc09e5d76443a36fbaf706f48"},"schema_version":"1.0"},"canonical_sha256":"e64875fbc4855fcbf38a6126f925aa337dc62e81cf48814e2cbc4cdfc6793b77","source":{"kind":"arxiv","id":"2410.00385","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.00385","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"arxiv_version","alias_value":"2410.00385v2","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.00385","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"pith_short_12","alias_value":"4ZEHL66EQVP4","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"pith_short_16","alias_value":"4ZEHL66EQVP4X44K","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"pith_short_8","alias_value":"4ZEHL66E","created_at":"2026-07-05T09:20:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4ZEHL66EQVP4X44KMETPSJNKGN","target":"record","payload":{"canonical_record":{"source":{"id":"2410.00385","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T04:15:48Z","cross_cats_sorted":["cs.AI","cs.DB"],"title_canon_sha256":"5bc697ea8db8dd95322a35810dedda45b29afde487196414fdcaa6a994c03eba","abstract_canon_sha256":"29f90d05e03a8330f0602030271d79dbba035edbc09e5d76443a36fbaf706f48"},"schema_version":"1.0"},"canonical_sha256":"e64875fbc4855fcbf38a6126f925aa337dc62e81cf48814e2cbc4cdfc6793b77","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:48.506669Z","signature_b64":"A0ap4aYxbkr4erYzittE8KFV79kUT5tGZIC2mUGz0a2td09jHC5dQ1MwBvBxYN/EiAK8vO0F3EhkusQRSShLDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e64875fbc4855fcbf38a6126f925aa337dc62e81cf48814e2cbc4cdfc6793b77","last_reissued_at":"2026-07-05T09:20:48.506308Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:48.506308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.00385","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":"2026-07-05T09:20:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2itMXe03H6BtPCIYo6xxTDE6AoLdQ47wNc+pobhH5WvCCmgV5YNBTh6ghO04ROZLQ09e6iSYgCZ4/bFVICWsCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T16:33:47.247303Z"},"content_sha256":"375de3c82b77096db90bbc5753777007839ee01b756a68b9c53b91d23017656f","schema_version":"1.0","event_id":"sha256:375de3c82b77096db90bbc5753777007839ee01b756a68b9c53b91d23017656f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4ZEHL66EQVP4X44KMETPSJNKGN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DB"],"primary_cat":"cs.LG","authors_text":"Hongjun Wang, Jiyuan Chen, Lingyu Zhang, Renhe Jiang, Tong Pan, Xuan Song, Zheng Dong","submitted_at":"2024-10-01T04:15:48Z","abstract_excerpt":"Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture complex nonlinear patterns in spatiotemporal (ST) data, has emerged as a powerful tool for traffic forecasting. While graph neural networks (GCNs) and transformer-based models have shown promise, their computational demands often hinder their application to real-world road networks, particularly those with large-scale spatiotemporal interactions. To address these challenges, we propose a novel spatiotemporal graph transfor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.00385","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/2410.00385/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":"2026-07-05T09:20:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XqRms8z/wrpTmiWC6lchOGup7RDtIkYS6d5SsIIeXhGnQjosIPnVOzEfQSL54PveyYSuGnOFmNvLdK8z/QlhCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T16:33:47.247818Z"},"content_sha256":"bb025c0d6a96ea92c8a39e3ffb4bda5ffa9d1e1a63bf48ffa35af8bb1856e0e7","schema_version":"1.0","event_id":"sha256:bb025c0d6a96ea92c8a39e3ffb4bda5ffa9d1e1a63bf48ffa35af8bb1856e0e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4ZEHL66EQVP4X44KMETPSJNKGN/bundle.json","state_url":"https://pith.science/pith/4ZEHL66EQVP4X44KMETPSJNKGN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4ZEHL66EQVP4X44KMETPSJNKGN/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":"2026-08-14T16:33:47Z","links":{"resolver":"https://pith.science/pith/4ZEHL66EQVP4X44KMETPSJNKGN","bundle":"https://pith.science/pith/4ZEHL66EQVP4X44KMETPSJNKGN/bundle.json","state":"https://pith.science/pith/4ZEHL66EQVP4X44KMETPSJNKGN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4ZEHL66EQVP4X44KMETPSJNKGN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4ZEHL66EQVP4X44KMETPSJNKGN","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":"29f90d05e03a8330f0602030271d79dbba035edbc09e5d76443a36fbaf706f48","cross_cats_sorted":["cs.AI","cs.DB"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T04:15:48Z","title_canon_sha256":"5bc697ea8db8dd95322a35810dedda45b29afde487196414fdcaa6a994c03eba"},"schema_version":"1.0","source":{"id":"2410.00385","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.00385","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"arxiv_version","alias_value":"2410.00385v2","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.00385","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"pith_short_12","alias_value":"4ZEHL66EQVP4","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"pith_short_16","alias_value":"4ZEHL66EQVP4X44K","created_at":"2026-07-05T09:20:48Z"},{"alias_kind":"pith_short_8","alias_value":"4ZEHL66E","created_at":"2026-07-05T09:20:48Z"}],"graph_snapshots":[{"event_id":"sha256:bb025c0d6a96ea92c8a39e3ffb4bda5ffa9d1e1a63bf48ffa35af8bb1856e0e7","target":"graph","created_at":"2026-07-05T09:20:48Z","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/2410.00385/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture complex nonlinear patterns in spatiotemporal (ST) data, has emerged as a powerful tool for traffic forecasting. While graph neural networks (GCNs) and transformer-based models have shown promise, their computational demands often hinder their application to real-world road networks, particularly those with large-scale spatiotemporal interactions. To address these challenges, we propose a novel spatiotemporal graph transfor","authors_text":"Hongjun Wang, Jiyuan Chen, Lingyu Zhang, Renhe Jiang, Tong Pan, Xuan Song, Zheng Dong","cross_cats":["cs.AI","cs.DB"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T04:15:48Z","title":"STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.00385","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":"sha256:375de3c82b77096db90bbc5753777007839ee01b756a68b9c53b91d23017656f","target":"record","created_at":"2026-07-05T09:20:48Z","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":"29f90d05e03a8330f0602030271d79dbba035edbc09e5d76443a36fbaf706f48","cross_cats_sorted":["cs.AI","cs.DB"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T04:15:48Z","title_canon_sha256":"5bc697ea8db8dd95322a35810dedda45b29afde487196414fdcaa6a994c03eba"},"schema_version":"1.0","source":{"id":"2410.00385","kind":"arxiv","version":2}},"canonical_sha256":"e64875fbc4855fcbf38a6126f925aa337dc62e81cf48814e2cbc4cdfc6793b77","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e64875fbc4855fcbf38a6126f925aa337dc62e81cf48814e2cbc4cdfc6793b77","first_computed_at":"2026-07-05T09:20:48.506308Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:20:48.506308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A0ap4aYxbkr4erYzittE8KFV79kUT5tGZIC2mUGz0a2td09jHC5dQ1MwBvBxYN/EiAK8vO0F3EhkusQRSShLDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:20:48.506669Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.00385","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:375de3c82b77096db90bbc5753777007839ee01b756a68b9c53b91d23017656f","sha256:bb025c0d6a96ea92c8a39e3ffb4bda5ffa9d1e1a63bf48ffa35af8bb1856e0e7"],"state_sha256":"e5360ef545e773a62b72b2be0ed615bbd0255e542aa95f7eeab5d73204316c81"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iMPMBy4+dORWbGPrWAKl8nniESRJ6vwclVS1rGhLaiyVwbLQv9j7srmNqZRNHccgsEbgXEuB0Jx11F66FidhDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T16:33:47.254878Z","bundle_sha256":"910c662a127e4b1bb89f94fcbc8b82e0a89ee6fc9680413ebb2a81262c7cb891"}}