{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BVT4XE3OZBEA4FTBRVO56C7WFZ","short_pith_number":"pith:BVT4XE3O","canonical_record":{"source":{"id":"1906.01629","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T17:59:40Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"8cc6293760b451d06c7962ea27fef07d22dcbc4febe26c6b0fd81a844539b2bd","abstract_canon_sha256":"167a019b5340b72012b3529d57d8eedcf55d9ddce911a37c574a371f722641e9"},"schema_version":"1.0"},"canonical_sha256":"0d67cb936ec8480e16618d5ddf0bf62e73a6834690a23b604f9748d78b096e52","source":{"kind":"arxiv","id":"1906.01629","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01629","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01629v3","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01629","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"pith_short_12","alias_value":"BVT4XE3OZBEA","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"pith_short_16","alias_value":"BVT4XE3OZBEA4FTB","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"pith_short_8","alias_value":"BVT4XE3O","created_at":"2026-07-05T00:15:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BVT4XE3OZBEA4FTBRVO56C7WFZ","target":"record","payload":{"canonical_record":{"source":{"id":"1906.01629","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T17:59:40Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"8cc6293760b451d06c7962ea27fef07d22dcbc4febe26c6b0fd81a844539b2bd","abstract_canon_sha256":"167a019b5340b72012b3529d57d8eedcf55d9ddce911a37c574a371f722641e9"},"schema_version":"1.0"},"canonical_sha256":"0d67cb936ec8480e16618d5ddf0bf62e73a6834690a23b604f9748d78b096e52","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:15:48.948888Z","signature_b64":"A3GNmgv8g6lqhugjgZEzXQGkU54Fj7J+/OR8/K1gAo/KBojxws6Ll4NV31QiUiMdOMLxw2QKHS0mkKykMRgtDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d67cb936ec8480e16618d5ddf0bf62e73a6834690a23b604f9748d78b096e52","last_reissued_at":"2026-07-05T00:15:48.948352Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:15:48.948352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.01629","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:15:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FT2vZ2IIapdBC+FU8VwhrVgjhzwSmKcwFHceP2csnXhhFc7P7KJOj1l/ZaFfrfJvSNasX3qQNSKvY7fkUXxnDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:11:44.818383Z"},"content_sha256":"d1f6260b460707984b86a8a320e61426c5dc6ce07e686236e0e35e56a548a98b","schema_version":"1.0","event_id":"sha256:d1f6260b460707984b86a8a320e61426c5dc6ce07e686236e0e35e56a548a98b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BVT4XE3OZBEA4FTBRVO56C7WFZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exact Combinatorial Optimization with Graph Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrea Lodi, Didier Ch\\'etelat, Laurent Charlin, Maxime Gasse, Nicola Ferroni","submitted_at":"2019-06-04T17:59:40Z","abstract_excerpt":"Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01629","kind":"arxiv","version":3},"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/1906.01629/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":"2026-07-05T00:15:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u4SQq2TW16ylSl6mXrNwM+IwN2irNPoSd8c2njHfOrLjx0vBzW88kMGp5tz6D3U5Zk4SZ2TkT2cPsN3S/y4UBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:11:44.818864Z"},"content_sha256":"cab5e16e46e87685017ed3edd38cc80dc2fd30ef82d030b373cfbade2e7d942a","schema_version":"1.0","event_id":"sha256:cab5e16e46e87685017ed3edd38cc80dc2fd30ef82d030b373cfbade2e7d942a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BVT4XE3OZBEA4FTBRVO56C7WFZ/bundle.json","state_url":"https://pith.science/pith/BVT4XE3OZBEA4FTBRVO56C7WFZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BVT4XE3OZBEA4FTBRVO56C7WFZ/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":"2026-08-05T03:11:44Z","links":{"resolver":"https://pith.science/pith/BVT4XE3OZBEA4FTBRVO56C7WFZ","bundle":"https://pith.science/pith/BVT4XE3OZBEA4FTBRVO56C7WFZ/bundle.json","state":"https://pith.science/pith/BVT4XE3OZBEA4FTBRVO56C7WFZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BVT4XE3OZBEA4FTBRVO56C7WFZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BVT4XE3OZBEA4FTBRVO56C7WFZ","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":"167a019b5340b72012b3529d57d8eedcf55d9ddce911a37c574a371f722641e9","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T17:59:40Z","title_canon_sha256":"8cc6293760b451d06c7962ea27fef07d22dcbc4febe26c6b0fd81a844539b2bd"},"schema_version":"1.0","source":{"id":"1906.01629","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01629","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01629v3","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01629","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"pith_short_12","alias_value":"BVT4XE3OZBEA","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"pith_short_16","alias_value":"BVT4XE3OZBEA4FTB","created_at":"2026-07-05T00:15:48Z"},{"alias_kind":"pith_short_8","alias_value":"BVT4XE3O","created_at":"2026-07-05T00:15:48Z"}],"graph_snapshots":[{"event_id":"sha256:cab5e16e46e87685017ed3edd38cc80dc2fd30ef82d030b373cfbade2e7d942a","target":"graph","created_at":"2026-07-05T00:15:48Z","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/1906.01629/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly ","authors_text":"Andrea Lodi, Didier Ch\\'etelat, Laurent Charlin, Maxime Gasse, Nicola Ferroni","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T17:59:40Z","title":"Exact Combinatorial Optimization with Graph Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01629","kind":"arxiv","version":3},"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:d1f6260b460707984b86a8a320e61426c5dc6ce07e686236e0e35e56a548a98b","target":"record","created_at":"2026-07-05T00:15:48Z","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":"167a019b5340b72012b3529d57d8eedcf55d9ddce911a37c574a371f722641e9","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T17:59:40Z","title_canon_sha256":"8cc6293760b451d06c7962ea27fef07d22dcbc4febe26c6b0fd81a844539b2bd"},"schema_version":"1.0","source":{"id":"1906.01629","kind":"arxiv","version":3}},"canonical_sha256":"0d67cb936ec8480e16618d5ddf0bf62e73a6834690a23b604f9748d78b096e52","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0d67cb936ec8480e16618d5ddf0bf62e73a6834690a23b604f9748d78b096e52","first_computed_at":"2026-07-05T00:15:48.948352Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:15:48.948352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A3GNmgv8g6lqhugjgZEzXQGkU54Fj7J+/OR8/K1gAo/KBojxws6Ll4NV31QiUiMdOMLxw2QKHS0mkKykMRgtDw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:15:48.948888Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.01629","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1f6260b460707984b86a8a320e61426c5dc6ce07e686236e0e35e56a548a98b","sha256:cab5e16e46e87685017ed3edd38cc80dc2fd30ef82d030b373cfbade2e7d942a"],"state_sha256":"dde0e719a2d5101c7d6a719cf9ba720697b3337773f5b3ed0fcc000d965ec162"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FeaYm7j1CF5F5SabnVMWb9hqdi6q3aSSNzLveth7Nz9uxClMWkUW5uTNIPCPqqIszLF2TYgAjTI+0nyWL7MjDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:11:44.824943Z","bundle_sha256":"addde05942d2b9e07b4b01c70b2174d387eee7976e9fd9d971b1e2a68b9cb586"}}