{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BQYKQLE54ZTLNPQNK6EZYTMO2J","short_pith_number":"pith:BQYKQLE5","schema_version":"1.0","canonical_sha256":"0c30a82c9de666b6be0d57899c4d8ed242a3db0c7c602ac86d4e6bd644514bb7","source":{"kind":"arxiv","id":"1807.02963","version":1},"attestation_state":"computed","paper":{"title":"Jointly learning relevant subgraph patterns and nonlinear models of their indicators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fumiya Okazaki, Ichigaku Takigawa, Ryo Shirakawa, Yusei Yokoyama","submitted_at":"2018-07-09T06:56:22Z","abstract_excerpt":"Classification and regression in which the inputs are graphs of arbitrary size and shape have been paid attention in various fields such as computational chemistry and bioinformatics. Subgraph indicators are often used as the most fundamental features, but the number of possible subgraph patterns are intractably large due to the combinatorial explosion. We propose a novel efficient algorithm to jointly learn relevant subgraph patterns and nonlinear models of their indicators. Previous methods for such joint learning of subgraph features and models are based on search for single best subgraph f"},"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":"1807.02963","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-09T06:56:22Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"4faaffb245899e7fae4f8e24acfb43072a8e17dfff9c79255e746266fa4f4152","abstract_canon_sha256":"d972c3d84d97d7db1ae22ab334070598ed3ae662e13a593fe8799f35cebd92af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:13.222407Z","signature_b64":"fQesu34o182DKQK2OTNK1M1IaVYsOoMepzIVHyLU2EFT8WVzI5OBZMpGUZ+KPmiZ7ivplwlw2cbLJWGVp9yQCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c30a82c9de666b6be0d57899c4d8ed242a3db0c7c602ac86d4e6bd644514bb7","last_reissued_at":"2026-05-18T00:11:13.221737Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:13.221737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Jointly learning relevant subgraph patterns and nonlinear models of their indicators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fumiya Okazaki, Ichigaku Takigawa, Ryo Shirakawa, Yusei Yokoyama","submitted_at":"2018-07-09T06:56:22Z","abstract_excerpt":"Classification and regression in which the inputs are graphs of arbitrary size and shape have been paid attention in various fields such as computational chemistry and bioinformatics. Subgraph indicators are often used as the most fundamental features, but the number of possible subgraph patterns are intractably large due to the combinatorial explosion. We propose a novel efficient algorithm to jointly learn relevant subgraph patterns and nonlinear models of their indicators. Previous methods for such joint learning of subgraph features and models are based on search for single best subgraph f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02963","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":""},"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":"1807.02963","created_at":"2026-05-18T00:11:13.221830+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.02963v1","created_at":"2026-05-18T00:11:13.221830+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02963","created_at":"2026-05-18T00:11:13.221830+00:00"},{"alias_kind":"pith_short_12","alias_value":"BQYKQLE54ZTL","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"BQYKQLE54ZTLNPQN","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"BQYKQLE5","created_at":"2026-05-18T12:32:16.446611+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/BQYKQLE54ZTLNPQNK6EZYTMO2J","json":"https://pith.science/pith/BQYKQLE54ZTLNPQNK6EZYTMO2J.json","graph_json":"https://pith.science/api/pith-number/BQYKQLE54ZTLNPQNK6EZYTMO2J/graph.json","events_json":"https://pith.science/api/pith-number/BQYKQLE54ZTLNPQNK6EZYTMO2J/events.json","paper":"https://pith.science/paper/BQYKQLE5"},"agent_actions":{"view_html":"https://pith.science/pith/BQYKQLE54ZTLNPQNK6EZYTMO2J","download_json":"https://pith.science/pith/BQYKQLE54ZTLNPQNK6EZYTMO2J.json","view_paper":"https://pith.science/paper/BQYKQLE5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.02963&json=true","fetch_graph":"https://pith.science/api/pith-number/BQYKQLE54ZTLNPQNK6EZYTMO2J/graph.json","fetch_events":"https://pith.science/api/pith-number/BQYKQLE54ZTLNPQNK6EZYTMO2J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BQYKQLE54ZTLNPQNK6EZYTMO2J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BQYKQLE54ZTLNPQNK6EZYTMO2J/action/storage_attestation","attest_author":"https://pith.science/pith/BQYKQLE54ZTLNPQNK6EZYTMO2J/action/author_attestation","sign_citation":"https://pith.science/pith/BQYKQLE54ZTLNPQNK6EZYTMO2J/action/citation_signature","submit_replication":"https://pith.science/pith/BQYKQLE54ZTLNPQNK6EZYTMO2J/action/replication_record"}},"created_at":"2026-05-18T00:11:13.221830+00:00","updated_at":"2026-05-18T00:11:13.221830+00:00"}