{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:ES5GL2XNFNVZOOLELJ63GQHICV","short_pith_number":"pith:ES5GL2XN","canonical_record":{"source":{"id":"2109.00924","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-29T13:07:18Z","cross_cats_sorted":[],"title_canon_sha256":"ed1e5c9f9cc96d6e91c2cfe2d6ec702ca93d86b0facf0d841cf5d87c66f89153","abstract_canon_sha256":"8f6b4b189a2e10db4c5c7b1905a519278b0c9cba84fef0dab97c95d0d1bbcd40"},"schema_version":"1.0"},"canonical_sha256":"24ba65eaed2b6b9739645a7db340e81574d234304c2ef714166cabfd172f5a9d","source":{"kind":"arxiv","id":"2109.00924","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.00924","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"arxiv_version","alias_value":"2109.00924v1","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.00924","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_12","alias_value":"ES5GL2XNFNVZ","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_16","alias_value":"ES5GL2XNFNVZOOLE","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_8","alias_value":"ES5GL2XN","created_at":"2026-07-05T03:10:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:ES5GL2XNFNVZOOLELJ63GQHICV","target":"record","payload":{"canonical_record":{"source":{"id":"2109.00924","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-29T13:07:18Z","cross_cats_sorted":[],"title_canon_sha256":"ed1e5c9f9cc96d6e91c2cfe2d6ec702ca93d86b0facf0d841cf5d87c66f89153","abstract_canon_sha256":"8f6b4b189a2e10db4c5c7b1905a519278b0c9cba84fef0dab97c95d0d1bbcd40"},"schema_version":"1.0"},"canonical_sha256":"24ba65eaed2b6b9739645a7db340e81574d234304c2ef714166cabfd172f5a9d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:10:59.786496Z","signature_b64":"VrMxzXuC8Pir0YrHKqe905e79Vtc1tAyq5AMLuAH5lf3OoOPiXk7G6LRDmwXGr9qq8viMWZr5SoyLpl53c2yCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24ba65eaed2b6b9739645a7db340e81574d234304c2ef714166cabfd172f5a9d","last_reissued_at":"2026-07-05T03:10:59.786077Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:10:59.786077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.00924","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:10:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N/86VtKrrpyt37ZK65InxVN3r8Sot0fXLdkG57Jw/mkTE5OLMU8VwpB7Glx58idL+vybPBZOFUCPhFxv0AzECQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T13:35:41.349141Z"},"content_sha256":"d2dab670f5be672bcc2e7bea7cfde49a92a793add17688771cdd4735e6bb7e11","schema_version":"1.0","event_id":"sha256:d2dab670f5be672bcc2e7bea7cfde49a92a793add17688771cdd4735e6bb7e11"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:ES5GL2XNFNVZOOLELJ63GQHICV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fuchen Gao, Zhanquan Wang, Zhenguang Liu","submitted_at":"2021-08-29T13:07:18Z","abstract_excerpt":"Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.00924","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/2109.00924/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":"2026-07-05T03:10:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KfrevKJn/NZcSdtKm/Ly3mPs8Mp2i/oFMWm/sBZ7Ys61MKMV1bOwEPgl4LWclc36oaaeXaAWTPbqt4/tawF8Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T13:35:41.349453Z"},"content_sha256":"f1e549d64666721ad61ac68f33f346fb9cc3109007eddb94a3ea5720763fa8b7","schema_version":"1.0","event_id":"sha256:f1e549d64666721ad61ac68f33f346fb9cc3109007eddb94a3ea5720763fa8b7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ES5GL2XNFNVZOOLELJ63GQHICV/bundle.json","state_url":"https://pith.science/pith/ES5GL2XNFNVZOOLELJ63GQHICV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ES5GL2XNFNVZOOLELJ63GQHICV/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":"2026-07-31T13:35:41Z","links":{"resolver":"https://pith.science/pith/ES5GL2XNFNVZOOLELJ63GQHICV","bundle":"https://pith.science/pith/ES5GL2XNFNVZOOLELJ63GQHICV/bundle.json","state":"https://pith.science/pith/ES5GL2XNFNVZOOLELJ63GQHICV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ES5GL2XNFNVZOOLELJ63GQHICV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:ES5GL2XNFNVZOOLELJ63GQHICV","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":"8f6b4b189a2e10db4c5c7b1905a519278b0c9cba84fef0dab97c95d0d1bbcd40","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-29T13:07:18Z","title_canon_sha256":"ed1e5c9f9cc96d6e91c2cfe2d6ec702ca93d86b0facf0d841cf5d87c66f89153"},"schema_version":"1.0","source":{"id":"2109.00924","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.00924","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"arxiv_version","alias_value":"2109.00924v1","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.00924","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_12","alias_value":"ES5GL2XNFNVZ","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_16","alias_value":"ES5GL2XNFNVZOOLE","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_8","alias_value":"ES5GL2XN","created_at":"2026-07-05T03:10:59Z"}],"graph_snapshots":[{"event_id":"sha256:f1e549d64666721ad61ac68f33f346fb9cc3109007eddb94a3ea5720763fa8b7","target":"graph","created_at":"2026-07-05T03:10:59Z","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/2109.00924/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stat","authors_text":"Fuchen Gao, Zhanquan Wang, Zhenguang Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-29T13:07:18Z","title":"Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.00924","kind":"arxiv","version":1},"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:d2dab670f5be672bcc2e7bea7cfde49a92a793add17688771cdd4735e6bb7e11","target":"record","created_at":"2026-07-05T03:10:59Z","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":"8f6b4b189a2e10db4c5c7b1905a519278b0c9cba84fef0dab97c95d0d1bbcd40","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-29T13:07:18Z","title_canon_sha256":"ed1e5c9f9cc96d6e91c2cfe2d6ec702ca93d86b0facf0d841cf5d87c66f89153"},"schema_version":"1.0","source":{"id":"2109.00924","kind":"arxiv","version":1}},"canonical_sha256":"24ba65eaed2b6b9739645a7db340e81574d234304c2ef714166cabfd172f5a9d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24ba65eaed2b6b9739645a7db340e81574d234304c2ef714166cabfd172f5a9d","first_computed_at":"2026-07-05T03:10:59.786077Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:10:59.786077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VrMxzXuC8Pir0YrHKqe905e79Vtc1tAyq5AMLuAH5lf3OoOPiXk7G6LRDmwXGr9qq8viMWZr5SoyLpl53c2yCw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:10:59.786496Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.00924","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d2dab670f5be672bcc2e7bea7cfde49a92a793add17688771cdd4735e6bb7e11","sha256:f1e549d64666721ad61ac68f33f346fb9cc3109007eddb94a3ea5720763fa8b7"],"state_sha256":"b3f02e186ba2b73a8f65d5a5c2120bb2947eee4985fc2b27eedb1bd8565c7de9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nCuMqLHSmooG4Fhs0exu/xQnKwi/GBwD86gS/Yhz7lM1+dmZWyIROoJxiE0KlOddWqnJhD/36CR5qovOfuufDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T13:35:41.352336Z","bundle_sha256":"cd1194d737bd0e9a6fc93e8acc38772cc5fab5d66424281b0536df59ddc26697"}}