{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YBTSQFHE6MG3LXJS7LRPZS2BPL","short_pith_number":"pith:YBTSQFHE","schema_version":"1.0","canonical_sha256":"c0672814e4f30db5dd32fae2fccb417af2fb80ddf9fc273744113d5408783b29","source":{"kind":"arxiv","id":"1908.07645","version":4},"attestation_state":"computed","paper":{"title":"K-Nearest Neighbor Approximation Via the Friend-of-a-Friend Principle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"math.CO","authors_text":"Jacob D. Baron, R. W. R. Darling","submitted_at":"2019-08-20T23:43:24Z","abstract_excerpt":"Suppose $V$ is an $n$-element set where for each $x \\in V$, the elements of $V \\setminus \\{x\\}$ are ranked by their similarity to $x$. The $K$-nearest neighbor graph is a directed graph including an arc from each $x$ to the $K$ points of $V \\setminus \\{x\\}$ most similar to $x$. Constructive approximation to this graph using far fewer than $n^2$ comparisons is important for the analysis of large high-dimensional data sets. $K$-Nearest Neighbor Descent is a parameter-free heuristic where a sequence of graph approximations is constructed, in which second neighbors in one approximation are propose"},"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":"1908.07645","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.CO","submitted_at":"2019-08-20T23:43:24Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"42b95aa66999fbd6419e88f4c8d240f2871efdadfab92c14a1a482569066ebab","abstract_canon_sha256":"0655257eab76a22c45cf2dfd16a7a636e2c2fc00d3b8d9446f86c08b97acfc1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:02:00.831164Z","signature_b64":"UxXpgJZJT/ZFWtZc4P/QQUx+62HHXYCjWWvjRSZ/3CCtooaiVKGA4y4CZ300BrkgVUgUGPklZ7XgrdbaxyORDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0672814e4f30db5dd32fae2fccb417af2fb80ddf9fc273744113d5408783b29","last_reissued_at":"2026-07-05T02:02:00.830763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:02:00.830763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"K-Nearest Neighbor Approximation Via the Friend-of-a-Friend Principle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"math.CO","authors_text":"Jacob D. Baron, R. W. R. Darling","submitted_at":"2019-08-20T23:43:24Z","abstract_excerpt":"Suppose $V$ is an $n$-element set where for each $x \\in V$, the elements of $V \\setminus \\{x\\}$ are ranked by their similarity to $x$. The $K$-nearest neighbor graph is a directed graph including an arc from each $x$ to the $K$ points of $V \\setminus \\{x\\}$ most similar to $x$. Constructive approximation to this graph using far fewer than $n^2$ comparisons is important for the analysis of large high-dimensional data sets. $K$-Nearest Neighbor Descent is a parameter-free heuristic where a sequence of graph approximations is constructed, in which second neighbors in one approximation are propose"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07645","kind":"arxiv","version":4},"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/1908.07645/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":"1908.07645","created_at":"2026-07-05T02:02:00.830817+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.07645v4","created_at":"2026-07-05T02:02:00.830817+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07645","created_at":"2026-07-05T02:02:00.830817+00:00"},{"alias_kind":"pith_short_12","alias_value":"YBTSQFHE6MG3","created_at":"2026-07-05T02:02:00.830817+00:00"},{"alias_kind":"pith_short_16","alias_value":"YBTSQFHE6MG3LXJS","created_at":"2026-07-05T02:02:00.830817+00:00"},{"alias_kind":"pith_short_8","alias_value":"YBTSQFHE","created_at":"2026-07-05T02:02:00.830817+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.14261","citing_title":"FAMST: Fast Approximate Minimum Spanning Tree Construction for Large-Scale and High-Dimensional Data","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL","json":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL.json","graph_json":"https://pith.science/api/pith-number/YBTSQFHE6MG3LXJS7LRPZS2BPL/graph.json","events_json":"https://pith.science/api/pith-number/YBTSQFHE6MG3LXJS7LRPZS2BPL/events.json","paper":"https://pith.science/paper/YBTSQFHE"},"agent_actions":{"view_html":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL","download_json":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL.json","view_paper":"https://pith.science/paper/YBTSQFHE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.07645&json=true","fetch_graph":"https://pith.science/api/pith-number/YBTSQFHE6MG3LXJS7LRPZS2BPL/graph.json","fetch_events":"https://pith.science/api/pith-number/YBTSQFHE6MG3LXJS7LRPZS2BPL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL/action/storage_attestation","attest_author":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL/action/author_attestation","sign_citation":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL/action/citation_signature","submit_replication":"https://pith.science/pith/YBTSQFHE6MG3LXJS7LRPZS2BPL/action/replication_record"}},"created_at":"2026-07-05T02:02:00.830817+00:00","updated_at":"2026-07-05T02:02:00.830817+00:00"}