{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WXO2XNON2JWA4PKSMQ4HLTJ3QG","short_pith_number":"pith:WXO2XNON","schema_version":"1.0","canonical_sha256":"b5ddabb5cdd26c0e3d52643875cd3b818dc47d1e2ec842bef7662d0725dbd000","source":{"kind":"arxiv","id":"2007.03196","version":1},"attestation_state":"computed","paper":{"title":"ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Cheekong Lee, Chengqiang Lu, Enhong Chen, Hao Wang, Qi Liu, Zhenya Huang, Zheyuan Hu, Zhongkai Hao","submitted_at":"2020-07-07T04:22:39Z","abstract_excerpt":"Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of scarce labeled molecules in the chemical space, where such property labels are generally obtained by Density Functional Theory (DFT) calculation which is extremely computational costly. An effective solution is to incorporate the unlabeled molecules in a semi-supervised fashion. However, learning semi-supervised representation for large amounts of molecules is challenging, including the joint representation issue of bo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2007.03196","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-07-07T04:22:39Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"435100a1305da4789ab549b8705392fd49d4af4978a4aca17b32486b4aed4c6a","abstract_canon_sha256":"a7e03a6c81f1555eab3219cc031d96f1f1ba5d9d115416a7e68cde047c7b5150"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:17:00.535870Z","signature_b64":"RV9Kvr3Msmw65WiaLF4QJkahWoRx8sGctbS9fM9m8ELBOilzGAwsT3r/DzWzfeJZd0nXjlrx+Lx4186PYTIRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5ddabb5cdd26c0e3d52643875cd3b818dc47d1e2ec842bef7662d0725dbd000","last_reissued_at":"2026-07-05T01:17:00.535476Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:17:00.535476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Cheekong Lee, Chengqiang Lu, Enhong Chen, Hao Wang, Qi Liu, Zhenya Huang, Zheyuan Hu, Zhongkai Hao","submitted_at":"2020-07-07T04:22:39Z","abstract_excerpt":"Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of scarce labeled molecules in the chemical space, where such property labels are generally obtained by Density Functional Theory (DFT) calculation which is extremely computational costly. An effective solution is to incorporate the unlabeled molecules in a semi-supervised fashion. However, learning semi-supervised representation for large amounts of molecules is challenging, including the joint representation issue of bo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.03196","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/2007.03196/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2007.03196","created_at":"2026-07-05T01:17:00.535535+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.03196v1","created_at":"2026-07-05T01:17:00.535535+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.03196","created_at":"2026-07-05T01:17:00.535535+00:00"},{"alias_kind":"pith_short_12","alias_value":"WXO2XNON2JWA","created_at":"2026-07-05T01:17:00.535535+00:00"},{"alias_kind":"pith_short_16","alias_value":"WXO2XNON2JWA4PKS","created_at":"2026-07-05T01:17:00.535535+00:00"},{"alias_kind":"pith_short_8","alias_value":"WXO2XNON","created_at":"2026-07-05T01:17:00.535535+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG","json":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG.json","graph_json":"https://pith.science/api/pith-number/WXO2XNON2JWA4PKSMQ4HLTJ3QG/graph.json","events_json":"https://pith.science/api/pith-number/WXO2XNON2JWA4PKSMQ4HLTJ3QG/events.json","paper":"https://pith.science/paper/WXO2XNON"},"agent_actions":{"view_html":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG","download_json":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG.json","view_paper":"https://pith.science/paper/WXO2XNON","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.03196&json=true","fetch_graph":"https://pith.science/api/pith-number/WXO2XNON2JWA4PKSMQ4HLTJ3QG/graph.json","fetch_events":"https://pith.science/api/pith-number/WXO2XNON2JWA4PKSMQ4HLTJ3QG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG/action/storage_attestation","attest_author":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG/action/author_attestation","sign_citation":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG/action/citation_signature","submit_replication":"https://pith.science/pith/WXO2XNON2JWA4PKSMQ4HLTJ3QG/action/replication_record"}},"created_at":"2026-07-05T01:17:00.535535+00:00","updated_at":"2026-07-05T01:17:00.535535+00:00"}