{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WO7BHCXUDMTFWZBJRUOHZTDNFT","short_pith_number":"pith:WO7BHCXU","canonical_record":{"source":{"id":"2306.07019","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-12T10:46:31Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"007a5a5c4c8741b5356b84b507a7def1e93fa4a7e7daa46d5daac05ddf598a06","abstract_canon_sha256":"907ae65f297adb14da5c87b09dd2f0a7787b7533fb9c33ddde55518c4c8115bf"},"schema_version":"1.0"},"canonical_sha256":"b3be138af41b265b64298d1c7ccc6d2cf1862f0717d429d1032a1a57da13b550","source":{"kind":"arxiv","id":"2306.07019","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.07019","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"arxiv_version","alias_value":"2306.07019v2","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07019","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"pith_short_12","alias_value":"WO7BHCXUDMTF","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"pith_short_16","alias_value":"WO7BHCXUDMTFWZBJ","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"pith_short_8","alias_value":"WO7BHCXU","created_at":"2026-07-05T06:48:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WO7BHCXUDMTFWZBJRUOHZTDNFT","target":"record","payload":{"canonical_record":{"source":{"id":"2306.07019","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-12T10:46:31Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"007a5a5c4c8741b5356b84b507a7def1e93fa4a7e7daa46d5daac05ddf598a06","abstract_canon_sha256":"907ae65f297adb14da5c87b09dd2f0a7787b7533fb9c33ddde55518c4c8115bf"},"schema_version":"1.0"},"canonical_sha256":"b3be138af41b265b64298d1c7ccc6d2cf1862f0717d429d1032a1a57da13b550","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:48:35.202878Z","signature_b64":"rLf92gKTO+kn7uZGEB8gschVpfsCqZNcCNWNMrCL68m7SwKcq52vJBnNjbtBonvvKuW4O/oxhc3b1L5h9Y8yCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3be138af41b265b64298d1c7ccc6d2cf1862f0717d429d1032a1a57da13b550","last_reissued_at":"2026-07-05T06:48:35.202421Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:48:35.202421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.07019","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":"2026-07-05T06:48:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8sBYIyQN7nMCoRvZnzAAVwCnEevYEUysggIhLS7eo1pA89Tx1aFbBDKCuwebKH3N8iK7rw/v0sCGu9nmU9L7BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:49:26.383914Z"},"content_sha256":"e6169ea121de286b8ffac16b7e3117096cab6ff6beae3e58e2015fa2f67ede82","schema_version":"1.0","event_id":"sha256:e6169ea121de286b8ffac16b7e3117096cab6ff6beae3e58e2015fa2f67ede82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WO7BHCXUDMTFWZBJRUOHZTDNFT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Causal Graph Convolutional Network for Traffic Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Chen Zhang, Junpeng Lin, Lei Bai, Rui Zhao, Zhishuai Li, Ziyue Li","submitted_at":"2023-06-12T10:46:31Z","abstract_excerpt":"Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to extract spatiotemporal correlations, their effectiveness depends on the quality of the graph structures used to represent the spatial topology of the traffic network. In this work, we propose a novel approach for traffic prediction that embeds time-varying dynamic Bayesian network to capture the fine spatiotemporal topology of traffic data. We then use graph convolutional networks to generate tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07019","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/2306.07019/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":"2026-07-05T06:48:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bktIlsQBSH2PpU7ze0LXsf4zVmkTX6T5IuCJ/bc8/nrNjIttM0A3x8DfJBkRb78yJmmMVC0It0E2TXBBY8WGAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:49:26.385086Z"},"content_sha256":"341514121de7fa9d18d9580c5627be93a4d52f6a35b206ca45321d7842e3915e","schema_version":"1.0","event_id":"sha256:341514121de7fa9d18d9580c5627be93a4d52f6a35b206ca45321d7842e3915e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WO7BHCXUDMTFWZBJRUOHZTDNFT/bundle.json","state_url":"https://pith.science/pith/WO7BHCXUDMTFWZBJRUOHZTDNFT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WO7BHCXUDMTFWZBJRUOHZTDNFT/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":"2026-08-06T08:49:26Z","links":{"resolver":"https://pith.science/pith/WO7BHCXUDMTFWZBJRUOHZTDNFT","bundle":"https://pith.science/pith/WO7BHCXUDMTFWZBJRUOHZTDNFT/bundle.json","state":"https://pith.science/pith/WO7BHCXUDMTFWZBJRUOHZTDNFT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WO7BHCXUDMTFWZBJRUOHZTDNFT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WO7BHCXUDMTFWZBJRUOHZTDNFT","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":"907ae65f297adb14da5c87b09dd2f0a7787b7533fb9c33ddde55518c4c8115bf","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-12T10:46:31Z","title_canon_sha256":"007a5a5c4c8741b5356b84b507a7def1e93fa4a7e7daa46d5daac05ddf598a06"},"schema_version":"1.0","source":{"id":"2306.07019","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.07019","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"arxiv_version","alias_value":"2306.07019v2","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07019","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"pith_short_12","alias_value":"WO7BHCXUDMTF","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"pith_short_16","alias_value":"WO7BHCXUDMTFWZBJ","created_at":"2026-07-05T06:48:35Z"},{"alias_kind":"pith_short_8","alias_value":"WO7BHCXU","created_at":"2026-07-05T06:48:35Z"}],"graph_snapshots":[{"event_id":"sha256:341514121de7fa9d18d9580c5627be93a4d52f6a35b206ca45321d7842e3915e","target":"graph","created_at":"2026-07-05T06:48:35Z","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/2306.07019/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to extract spatiotemporal correlations, their effectiveness depends on the quality of the graph structures used to represent the spatial topology of the traffic network. In this work, we propose a novel approach for traffic prediction that embeds time-varying dynamic Bayesian network to capture the fine spatiotemporal topology of traffic data. We then use graph convolutional networks to generate tra","authors_text":"Chen Zhang, Junpeng Lin, Lei Bai, Rui Zhao, Zhishuai Li, Ziyue Li","cross_cats":["eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-12T10:46:31Z","title":"Dynamic Causal Graph Convolutional Network for Traffic Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07019","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:e6169ea121de286b8ffac16b7e3117096cab6ff6beae3e58e2015fa2f67ede82","target":"record","created_at":"2026-07-05T06:48:35Z","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":"907ae65f297adb14da5c87b09dd2f0a7787b7533fb9c33ddde55518c4c8115bf","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-12T10:46:31Z","title_canon_sha256":"007a5a5c4c8741b5356b84b507a7def1e93fa4a7e7daa46d5daac05ddf598a06"},"schema_version":"1.0","source":{"id":"2306.07019","kind":"arxiv","version":2}},"canonical_sha256":"b3be138af41b265b64298d1c7ccc6d2cf1862f0717d429d1032a1a57da13b550","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3be138af41b265b64298d1c7ccc6d2cf1862f0717d429d1032a1a57da13b550","first_computed_at":"2026-07-05T06:48:35.202421Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:48:35.202421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rLf92gKTO+kn7uZGEB8gschVpfsCqZNcCNWNMrCL68m7SwKcq52vJBnNjbtBonvvKuW4O/oxhc3b1L5h9Y8yCA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:48:35.202878Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.07019","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e6169ea121de286b8ffac16b7e3117096cab6ff6beae3e58e2015fa2f67ede82","sha256:341514121de7fa9d18d9580c5627be93a4d52f6a35b206ca45321d7842e3915e"],"state_sha256":"8c219db47f7c6a3f2bfd527aac6312ade0da2d24b39a66a317075e678d9fb498"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jdg3UNxsXcBBDkWbp3pcwvbFU4nNCJtRlw26v3gwqease+ldae7qTMY4gOhDfhSP+MwqLAz1Uvf1IJBaZsmpBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T08:49:26.390976Z","bundle_sha256":"4f72203b11b61105783d3dec5e8548e476b6157e42b7f8a7ce199a90b6d95efa"}}