{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:A6VOX6GCJE6BW7QGKMTIHVFOOC","short_pith_number":"pith:A6VOX6GC","canonical_record":{"source":{"id":"1904.09981","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-22T07:13:10Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"c20716e9386ef3231ebfe42b2c7a07f74ab34d72caa9fafec766c65a04d529ae","abstract_canon_sha256":"68e2334b042e357c193d3c6779861e3a43498264d30f91998a9835fea44248a9"},"schema_version":"1.0"},"canonical_sha256":"07aaebf8c2493c1b7e06532683d4ae708824f3c995018f26e66f8d46e1923bf7","source":{"kind":"arxiv","id":"1904.09981","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.09981","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"arxiv_version","alias_value":"1904.09981v2","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.09981","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"pith_short_12","alias_value":"A6VOX6GCJE6B","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"pith_short_16","alias_value":"A6VOX6GCJE6BW7QG","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"pith_short_8","alias_value":"A6VOX6GC","created_at":"2026-07-05T01:48:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:A6VOX6GCJE6BW7QGKMTIHVFOOC","target":"record","payload":{"canonical_record":{"source":{"id":"1904.09981","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-22T07:13:10Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"c20716e9386ef3231ebfe42b2c7a07f74ab34d72caa9fafec766c65a04d529ae","abstract_canon_sha256":"68e2334b042e357c193d3c6779861e3a43498264d30f91998a9835fea44248a9"},"schema_version":"1.0"},"canonical_sha256":"07aaebf8c2493c1b7e06532683d4ae708824f3c995018f26e66f8d46e1923bf7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:48:05.287026Z","signature_b64":"b2eJsTXyhAAwaJYl+PXo7vVwqwKnblwhUnaBvsMFrsUxNT38UZakwyVloTM/XG0Mj7jrDmcnOeyqd1UtWIJmCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07aaebf8c2493c1b7e06532683d4ae708824f3c995018f26e66f8d46e1923bf7","last_reissued_at":"2026-07-05T01:48:05.286668Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:48:05.286668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.09981","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":"2026-07-05T01:48:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CdYOgZxO1mh1/0EL7RrKTb1gIIFpsEj5h0l50vJ1ds3vOnMU2QsKoUNDEwK34x3dI6kFlB743EIhZjh2OtHlCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T14:56:50.722355Z"},"content_sha256":"f37efc3d96cdab0c1601d6329aae08cd5017af25d1b39cba105144cfc515bcd0","schema_version":"1.0","event_id":"sha256:f37efc3d96cdab0c1601d6329aae08cd5017af25d1b39cba105144cfc515bcd0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:A6VOX6GCJE6BW7QGKMTIHVFOOC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GraphNAS: Graph Neural Architecture Search with Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chuan Zhou, Hong Yang, Peng Zhang, Yang Gao, Yue Hu","submitted_at":"2019-04-22T07:13:10Z","abstract_excerpt":"Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.09981","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/1904.09981/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":"2026-07-05T01:48:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D2QCkJZhErFVN8zwfew43wUEEuCTH9duwzpDNGsJwcOoruKbKuNOBWYiTud9jFcCS4p0gdnzWO0UTwTI8L+dDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T14:56:50.722880Z"},"content_sha256":"d8bdfebba867b45f1aa9611412e38047d251b8dd3203add87cfcacc3cca1760b","schema_version":"1.0","event_id":"sha256:d8bdfebba867b45f1aa9611412e38047d251b8dd3203add87cfcacc3cca1760b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A6VOX6GCJE6BW7QGKMTIHVFOOC/bundle.json","state_url":"https://pith.science/pith/A6VOX6GCJE6BW7QGKMTIHVFOOC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A6VOX6GCJE6BW7QGKMTIHVFOOC/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":"2026-08-14T14:56:50Z","links":{"resolver":"https://pith.science/pith/A6VOX6GCJE6BW7QGKMTIHVFOOC","bundle":"https://pith.science/pith/A6VOX6GCJE6BW7QGKMTIHVFOOC/bundle.json","state":"https://pith.science/pith/A6VOX6GCJE6BW7QGKMTIHVFOOC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A6VOX6GCJE6BW7QGKMTIHVFOOC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:A6VOX6GCJE6BW7QGKMTIHVFOOC","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":"68e2334b042e357c193d3c6779861e3a43498264d30f91998a9835fea44248a9","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-22T07:13:10Z","title_canon_sha256":"c20716e9386ef3231ebfe42b2c7a07f74ab34d72caa9fafec766c65a04d529ae"},"schema_version":"1.0","source":{"id":"1904.09981","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.09981","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"arxiv_version","alias_value":"1904.09981v2","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.09981","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"pith_short_12","alias_value":"A6VOX6GCJE6B","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"pith_short_16","alias_value":"A6VOX6GCJE6BW7QG","created_at":"2026-07-05T01:48:05Z"},{"alias_kind":"pith_short_8","alias_value":"A6VOX6GC","created_at":"2026-07-05T01:48:05Z"}],"graph_snapshots":[{"event_id":"sha256:d8bdfebba867b45f1aa9611412e38047d251b8dd3203add87cfcacc3cca1760b","target":"graph","created_at":"2026-07-05T01:48:05Z","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/1904.09981/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains","authors_text":"Chuan Zhou, Hong Yang, Peng Zhang, Yang Gao, Yue Hu","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-22T07:13:10Z","title":"GraphNAS: Graph Neural Architecture Search with Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.09981","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:f37efc3d96cdab0c1601d6329aae08cd5017af25d1b39cba105144cfc515bcd0","target":"record","created_at":"2026-07-05T01:48:05Z","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":"68e2334b042e357c193d3c6779861e3a43498264d30f91998a9835fea44248a9","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-22T07:13:10Z","title_canon_sha256":"c20716e9386ef3231ebfe42b2c7a07f74ab34d72caa9fafec766c65a04d529ae"},"schema_version":"1.0","source":{"id":"1904.09981","kind":"arxiv","version":2}},"canonical_sha256":"07aaebf8c2493c1b7e06532683d4ae708824f3c995018f26e66f8d46e1923bf7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07aaebf8c2493c1b7e06532683d4ae708824f3c995018f26e66f8d46e1923bf7","first_computed_at":"2026-07-05T01:48:05.286668Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:48:05.286668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b2eJsTXyhAAwaJYl+PXo7vVwqwKnblwhUnaBvsMFrsUxNT38UZakwyVloTM/XG0Mj7jrDmcnOeyqd1UtWIJmCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:48:05.287026Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.09981","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f37efc3d96cdab0c1601d6329aae08cd5017af25d1b39cba105144cfc515bcd0","sha256:d8bdfebba867b45f1aa9611412e38047d251b8dd3203add87cfcacc3cca1760b"],"state_sha256":"1d6dd50df5a622c934a8f89aa64bedd78c156470d0f6fae37a0fd1bc42526225"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HImEKB/1hbCb1YecQQHt2tk/6FwdaiWvXV6gqVVYbb9qAFtGm5cecHzHK0fZRj9Gu+9B4IvgEmWNMUuZlIz3Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T14:56:50.727741Z","bundle_sha256":"047527a81cf8236f8033adca6ce69169960427235cd01df5141f0563a77c5677"}}