{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6UD5TUG5O3TO3XACP4CLXGLA3M","short_pith_number":"pith:6UD5TUG5","schema_version":"1.0","canonical_sha256":"f507d9d0dd76e6eddc027f04bb9960db0762c5696ff77e78f6a3eee99370930b","source":{"kind":"arxiv","id":"2411.05742","version":1},"attestation_state":"computed","paper":{"title":"Topology-aware Reinforcement Feature Space Reconstruction for Graph Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haoyue Bai, Kunpeng Liu, Wangyang Ying, Yanjie Fu","submitted_at":"2024-11-08T18:01:05Z","abstract_excerpt":"Feature space is an environment where data points are vectorized to represent the original dataset. Reconstructing a good feature space is essential to augment the AI power of data, improve model generalization, and increase the availability of downstream ML models. Existing literature, such as feature transformation and feature selection, is labor-intensive (e.g., heavy reliance on empirical experience) and mostly designed for tabular data. Moreover, these methods regard data samples as independent, which ignores the unique topological structure when applied to graph data, thus resulting in a"},"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":"2411.05742","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-08T18:01:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eb734606331163f030318973fc6def71b1f5dbe53097401061271082a9cb394c","abstract_canon_sha256":"bede9916171587f0cf5ba0974e597a72054367b03eadfbc1cd52ce6d1ac7f7dc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:33:06.057446Z","signature_b64":"0gwpPLPF+mJuqajOu9w/X2tzRgMQwdJLtbT6tQ4GERHvWjmvMCHJwFJOqErKk1mEBHEnVd+j6hRw5JJteVldCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f507d9d0dd76e6eddc027f04bb9960db0762c5696ff77e78f6a3eee99370930b","last_reissued_at":"2026-07-05T09:33:06.057002Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:33:06.057002Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Topology-aware Reinforcement Feature Space Reconstruction for Graph Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haoyue Bai, Kunpeng Liu, Wangyang Ying, Yanjie Fu","submitted_at":"2024-11-08T18:01:05Z","abstract_excerpt":"Feature space is an environment where data points are vectorized to represent the original dataset. Reconstructing a good feature space is essential to augment the AI power of data, improve model generalization, and increase the availability of downstream ML models. Existing literature, such as feature transformation and feature selection, is labor-intensive (e.g., heavy reliance on empirical experience) and mostly designed for tabular data. Moreover, these methods regard data samples as independent, which ignores the unique topological structure when applied to graph data, thus resulting in a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.05742","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/2411.05742/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":"2411.05742","created_at":"2026-07-05T09:33:06.057062+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.05742v1","created_at":"2026-07-05T09:33:06.057062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.05742","created_at":"2026-07-05T09:33:06.057062+00:00"},{"alias_kind":"pith_short_12","alias_value":"6UD5TUG5O3TO","created_at":"2026-07-05T09:33:06.057062+00:00"},{"alias_kind":"pith_short_16","alias_value":"6UD5TUG5O3TO3XAC","created_at":"2026-07-05T09:33:06.057062+00:00"},{"alias_kind":"pith_short_8","alias_value":"6UD5TUG5","created_at":"2026-07-05T09:33:06.057062+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/6UD5TUG5O3TO3XACP4CLXGLA3M","json":"https://pith.science/pith/6UD5TUG5O3TO3XACP4CLXGLA3M.json","graph_json":"https://pith.science/api/pith-number/6UD5TUG5O3TO3XACP4CLXGLA3M/graph.json","events_json":"https://pith.science/api/pith-number/6UD5TUG5O3TO3XACP4CLXGLA3M/events.json","paper":"https://pith.science/paper/6UD5TUG5"},"agent_actions":{"view_html":"https://pith.science/pith/6UD5TUG5O3TO3XACP4CLXGLA3M","download_json":"https://pith.science/pith/6UD5TUG5O3TO3XACP4CLXGLA3M.json","view_paper":"https://pith.science/paper/6UD5TUG5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.05742&json=true","fetch_graph":"https://pith.science/api/pith-number/6UD5TUG5O3TO3XACP4CLXGLA3M/graph.json","fetch_events":"https://pith.science/api/pith-number/6UD5TUG5O3TO3XACP4CLXGLA3M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6UD5TUG5O3TO3XACP4CLXGLA3M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6UD5TUG5O3TO3XACP4CLXGLA3M/action/storage_attestation","attest_author":"https://pith.science/pith/6UD5TUG5O3TO3XACP4CLXGLA3M/action/author_attestation","sign_citation":"https://pith.science/pith/6UD5TUG5O3TO3XACP4CLXGLA3M/action/citation_signature","submit_replication":"https://pith.science/pith/6UD5TUG5O3TO3XACP4CLXGLA3M/action/replication_record"}},"created_at":"2026-07-05T09:33:06.057062+00:00","updated_at":"2026-07-05T09:33:06.057062+00:00"}