{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:QV4XVSDC4NLHLPCGUL5DNO3ED4","short_pith_number":"pith:QV4XVSDC","canonical_record":{"source":{"id":"1509.09292","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2015-09-30T18:33:50Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"0f6af6a9c82baee3f0db4dad6bbb77ec4a8c22698036c6e8d7de0681a5d99754","abstract_canon_sha256":"6203968ef923d38a9102af6e8f09a3bca7ed78f0af40938728eef94b1f5fa9c7"},"schema_version":"1.0"},"canonical_sha256":"85797ac862e35675bc46a2fa36bb641f336cbea364e45002d81bd2a746bf1003","source":{"kind":"arxiv","id":"1509.09292","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.09292","created_at":"2026-05-18T01:27:56Z"},{"alias_kind":"arxiv_version","alias_value":"1509.09292v2","created_at":"2026-05-18T01:27:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.09292","created_at":"2026-05-18T01:27:56Z"},{"alias_kind":"pith_short_12","alias_value":"QV4XVSDC4NLH","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"QV4XVSDC4NLHLPCG","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"QV4XVSDC","created_at":"2026-05-18T12:29:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:QV4XVSDC4NLHLPCGUL5DNO3ED4","target":"record","payload":{"canonical_record":{"source":{"id":"1509.09292","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2015-09-30T18:33:50Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"0f6af6a9c82baee3f0db4dad6bbb77ec4a8c22698036c6e8d7de0681a5d99754","abstract_canon_sha256":"6203968ef923d38a9102af6e8f09a3bca7ed78f0af40938728eef94b1f5fa9c7"},"schema_version":"1.0"},"canonical_sha256":"85797ac862e35675bc46a2fa36bb641f336cbea364e45002d81bd2a746bf1003","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:27:56.797045Z","signature_b64":"EvCdiYgIa7CToDUFJTuftUzz0fXo5I2BBc0hMOQwoGPOhL1SKpvbdrHCT5JD+nmuD0sIUoDQ3JUs6spdVdcFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85797ac862e35675bc46a2fa36bb641f336cbea364e45002d81bd2a746bf1003","last_reissued_at":"2026-05-18T01:27:56.796293Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:27:56.796293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1509.09292","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":"2026-05-18T01:27:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AUthbPjHXRyasY1523B3SkVXdEynnvPePZtyj55LCceym3sbWCJnig5WqumhujiGdmv6G/cj/wa8je7LPT9DCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T13:52:12.468515Z"},"content_sha256":"a688096ad0ba0bba235ae9756c08b33a83d97034a7a7a66fbef0e8178786f8b5","schema_version":"1.0","event_id":"sha256:a688096ad0ba0bba235ae9756c08b33a83d97034a7a7a66fbef0e8178786f8b5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:QV4XVSDC4NLHLPCGUL5DNO3ED4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Al\\'an Aspuru-Guzik, David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael G\\'omez-Bombarelli, Ryan P. Adams, Timothy Hirzel","submitted_at":"2015-09-30T18:33:50Z","abstract_excerpt":"We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.09292","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":""},"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":"2026-05-18T01:27:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nKiac4yrQ3k3MTf7+6GGmginpaLocZzsPf3erU9zNvpZS9Hm30m0LAeLWmcdzqikobTkal5bRh/FDTlxp6xoDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T13:52:12.468878Z"},"content_sha256":"2f02cafc14b58e76907684517f42c5788c38f0f0bdb8340a5903bd9a63d4d375","schema_version":"1.0","event_id":"sha256:2f02cafc14b58e76907684517f42c5788c38f0f0bdb8340a5903bd9a63d4d375"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QV4XVSDC4NLHLPCGUL5DNO3ED4/bundle.json","state_url":"https://pith.science/pith/QV4XVSDC4NLHLPCGUL5DNO3ED4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QV4XVSDC4NLHLPCGUL5DNO3ED4/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":"2026-06-01T13:52:12Z","links":{"resolver":"https://pith.science/pith/QV4XVSDC4NLHLPCGUL5DNO3ED4","bundle":"https://pith.science/pith/QV4XVSDC4NLHLPCGUL5DNO3ED4/bundle.json","state":"https://pith.science/pith/QV4XVSDC4NLHLPCGUL5DNO3ED4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QV4XVSDC4NLHLPCGUL5DNO3ED4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:QV4XVSDC4NLHLPCGUL5DNO3ED4","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":"6203968ef923d38a9102af6e8f09a3bca7ed78f0af40938728eef94b1f5fa9c7","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2015-09-30T18:33:50Z","title_canon_sha256":"0f6af6a9c82baee3f0db4dad6bbb77ec4a8c22698036c6e8d7de0681a5d99754"},"schema_version":"1.0","source":{"id":"1509.09292","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.09292","created_at":"2026-05-18T01:27:56Z"},{"alias_kind":"arxiv_version","alias_value":"1509.09292v2","created_at":"2026-05-18T01:27:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.09292","created_at":"2026-05-18T01:27:56Z"},{"alias_kind":"pith_short_12","alias_value":"QV4XVSDC4NLH","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"QV4XVSDC4NLHLPCG","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"QV4XVSDC","created_at":"2026-05-18T12:29:39Z"}],"graph_snapshots":[{"event_id":"sha256:2f02cafc14b58e76907684517f42c5788c38f0f0bdb8340a5903bd9a63d4d375","target":"graph","created_at":"2026-05-18T01:27:56Z","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"},"paper":{"abstract_excerpt":"We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.","authors_text":"Al\\'an Aspuru-Guzik, David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael G\\'omez-Bombarelli, Ryan P. Adams, Timothy Hirzel","cross_cats":["cs.NE","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2015-09-30T18:33:50Z","title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.09292","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:a688096ad0ba0bba235ae9756c08b33a83d97034a7a7a66fbef0e8178786f8b5","target":"record","created_at":"2026-05-18T01:27:56Z","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":"6203968ef923d38a9102af6e8f09a3bca7ed78f0af40938728eef94b1f5fa9c7","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2015-09-30T18:33:50Z","title_canon_sha256":"0f6af6a9c82baee3f0db4dad6bbb77ec4a8c22698036c6e8d7de0681a5d99754"},"schema_version":"1.0","source":{"id":"1509.09292","kind":"arxiv","version":2}},"canonical_sha256":"85797ac862e35675bc46a2fa36bb641f336cbea364e45002d81bd2a746bf1003","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85797ac862e35675bc46a2fa36bb641f336cbea364e45002d81bd2a746bf1003","first_computed_at":"2026-05-18T01:27:56.796293Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:27:56.796293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EvCdiYgIa7CToDUFJTuftUzz0fXo5I2BBc0hMOQwoGPOhL1SKpvbdrHCT5JD+nmuD0sIUoDQ3JUs6spdVdcFAw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:27:56.797045Z","signed_message":"canonical_sha256_bytes"},"source_id":"1509.09292","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a688096ad0ba0bba235ae9756c08b33a83d97034a7a7a66fbef0e8178786f8b5","sha256:2f02cafc14b58e76907684517f42c5788c38f0f0bdb8340a5903bd9a63d4d375"],"state_sha256":"f8278cd5d333f3315411db15591c726e7d4aded1bb86671c2ecc3ff4351eab07"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7/LIJ+uwuVrr+74vNF9kUqMph5E79zLpJLp0qMzDQtp1hPWVBdWXjbWhmeIIYhztNDrZsNqIP73EZ6VE795tAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T13:52:12.470852Z","bundle_sha256":"cfe868bc3c02d01ca51a38c63da2466d203b24ceba6e5adb5b218262b0af410a"}}