{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PBS2AF4WTZJPKJWNFZFDQNZMPG","short_pith_number":"pith:PBS2AF4W","schema_version":"1.0","canonical_sha256":"7865a017969e52f526cd2e4a38372c79ad07502a2463fa1422a1a0c2d7be8283","source":{"kind":"arxiv","id":"2408.13899","version":1},"attestation_state":"computed","paper":{"title":"$\\boldsymbol{Steiner}$-Hardness: A Query Hardness Measure for Graph-Based ANN Indexes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Peng Wang, Qitong Wang, Themis Palpanas, Wei Wang, Xiaoxing Cheng, Zeyu Wang","submitted_at":"2024-08-25T17:44:17Z","abstract_excerpt":"Graph-based indexes have been widely employed to accelerate approximate similarity search of high-dimensional vectors. However, the performance of graph indexes to answer different queries varies vastly, leading to an unstable quality of service for downstream applications. This necessitates an effective measure to test query hardness on graph indexes. Nonetheless, popular distance-based hardness measures like LID lose their effects due to the ignorance of the graph structure. In this paper, we propose $Steiner$-hardness, a novel connection-based graph-native query hardness measure. Specifical"},"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":"2408.13899","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DB","submitted_at":"2024-08-25T17:44:17Z","cross_cats_sorted":[],"title_canon_sha256":"aff65870d0462b432ca8b2b26dd1a872c8cffebd530519bc7b5ba45b9fb76a25","abstract_canon_sha256":"7d69569512cdbf0ba56d78d66c84d1e10370e1fbadc74e9035a70ff587ed9bda"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:59:13.602197Z","signature_b64":"5cYJPp+j7s7fpmZCWW46NbrgwlRyPUxK6YD5+6N0XfBotq0MYg0anoKoiBXchN2M/aI/ig4NLnMD/QbHYIWLAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7865a017969e52f526cd2e4a38372c79ad07502a2463fa1422a1a0c2d7be8283","last_reissued_at":"2026-07-05T08:59:13.601793Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:59:13.601793Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"$\\boldsymbol{Steiner}$-Hardness: A Query Hardness Measure for Graph-Based ANN Indexes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Peng Wang, Qitong Wang, Themis Palpanas, Wei Wang, Xiaoxing Cheng, Zeyu Wang","submitted_at":"2024-08-25T17:44:17Z","abstract_excerpt":"Graph-based indexes have been widely employed to accelerate approximate similarity search of high-dimensional vectors. However, the performance of graph indexes to answer different queries varies vastly, leading to an unstable quality of service for downstream applications. This necessitates an effective measure to test query hardness on graph indexes. Nonetheless, popular distance-based hardness measures like LID lose their effects due to the ignorance of the graph structure. In this paper, we propose $Steiner$-hardness, a novel connection-based graph-native query hardness measure. Specifical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.13899","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/2408.13899/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":"2408.13899","created_at":"2026-07-05T08:59:13.601852+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.13899v1","created_at":"2026-07-05T08:59:13.601852+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.13899","created_at":"2026-07-05T08:59:13.601852+00:00"},{"alias_kind":"pith_short_12","alias_value":"PBS2AF4WTZJP","created_at":"2026-07-05T08:59:13.601852+00:00"},{"alias_kind":"pith_short_16","alias_value":"PBS2AF4WTZJPKJWN","created_at":"2026-07-05T08:59:13.601852+00:00"},{"alias_kind":"pith_short_8","alias_value":"PBS2AF4W","created_at":"2026-07-05T08:59:13.601852+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.06501","citing_title":"Survey of Filtered Approximate Nearest Neighbor Search over the Vector-Scalar Hybrid Data","ref_index":95,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG","json":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG.json","graph_json":"https://pith.science/api/pith-number/PBS2AF4WTZJPKJWNFZFDQNZMPG/graph.json","events_json":"https://pith.science/api/pith-number/PBS2AF4WTZJPKJWNFZFDQNZMPG/events.json","paper":"https://pith.science/paper/PBS2AF4W"},"agent_actions":{"view_html":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG","download_json":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG.json","view_paper":"https://pith.science/paper/PBS2AF4W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.13899&json=true","fetch_graph":"https://pith.science/api/pith-number/PBS2AF4WTZJPKJWNFZFDQNZMPG/graph.json","fetch_events":"https://pith.science/api/pith-number/PBS2AF4WTZJPKJWNFZFDQNZMPG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG/action/storage_attestation","attest_author":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG/action/author_attestation","sign_citation":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG/action/citation_signature","submit_replication":"https://pith.science/pith/PBS2AF4WTZJPKJWNFZFDQNZMPG/action/replication_record"}},"created_at":"2026-07-05T08:59:13.601852+00:00","updated_at":"2026-07-05T08:59:13.601852+00:00"}