{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:7YITCQXKGI7QVUVKNFHBW6ESGM","short_pith_number":"pith:7YITCQXK","canonical_record":{"source":{"id":"2112.02234","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DS","submitted_at":"2021-12-04T04:07:27Z","cross_cats_sorted":[],"title_canon_sha256":"a3d2b46c376be60dd41dbef8ba6cfe199e32710461062d4d2b4b13b7d865b845","abstract_canon_sha256":"95c9ad42e1dd49e428aeff6891f77a73f1da7fb0b48f30b2094c7fc17f527ce8"},"schema_version":"1.0"},"canonical_sha256":"fe113142ea323f0ad2aa694e1b78923312a82fc9d7c9a22ae042be924ed622ed","source":{"kind":"arxiv","id":"2112.02234","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.02234","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"arxiv_version","alias_value":"2112.02234v1","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02234","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"pith_short_12","alias_value":"7YITCQXKGI7Q","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"pith_short_16","alias_value":"7YITCQXKGI7QVUVK","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"pith_short_8","alias_value":"7YITCQXK","created_at":"2026-07-05T03:37:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:7YITCQXKGI7QVUVKNFHBW6ESGM","target":"record","payload":{"canonical_record":{"source":{"id":"2112.02234","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DS","submitted_at":"2021-12-04T04:07:27Z","cross_cats_sorted":[],"title_canon_sha256":"a3d2b46c376be60dd41dbef8ba6cfe199e32710461062d4d2b4b13b7d865b845","abstract_canon_sha256":"95c9ad42e1dd49e428aeff6891f77a73f1da7fb0b48f30b2094c7fc17f527ce8"},"schema_version":"1.0"},"canonical_sha256":"fe113142ea323f0ad2aa694e1b78923312a82fc9d7c9a22ae042be924ed622ed","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:37:50.249792Z","signature_b64":"7sA5XSLrLbD3LZkUsnv/CLdLBDuB4qftDhz26w3K6OajQS45w3HzZIS0ey7p3L+/ic0fPcy49Xty713FWWBcBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe113142ea323f0ad2aa694e1b78923312a82fc9d7c9a22ae042be924ed622ed","last_reissued_at":"2026-07-05T03:37:50.249392Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:37:50.249392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.02234","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:37:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w0GWjoWhzpyTKf+rVa9xcQBxGKw+6HZzlaMc//BMBl/W1wHpxsdg19sD+aNIdnoUZ5YcGoQKagC4YEaHWXBMDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T03:41:23.485968Z"},"content_sha256":"7e03b8556147ab128dc4373ddb35224cf96606bd998dfc77d85e95431a4c56eb","schema_version":"1.0","event_id":"sha256:7e03b8556147ab128dc4373ddb35224cf96606bd998dfc77d85e95431a4c56eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:7YITCQXKGI7QVUVKNFHBW6ESGM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Revisiting $k$-Nearest Neighbor Graph Construction on High-Dimensional Data : Experiments and Analyses","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Cheng Hong, Cui Jiangtao, Liu Yingfan","submitted_at":"2021-12-04T04:07:27Z","abstract_excerpt":"The $k$-nearest neighbor graph (KNNG) on high-dimensional data is a data structure widely used in many applications such as similarity search, dimension reduction and clustering. Due to its increasing popularity, several methods under the same framework have been proposed in the past decade. This framework contains two steps, i.e. building an initial KNNG (denoted as \\texttt{INIT}) and then refining it by neighborhood propagation (denoted as \\texttt{NBPG}). However, there remain several questions to be answered. First, it lacks a comprehensive experimental comparison among representative solut"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02234","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/2112.02234/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:37:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fjHFzNOwjXpOtv6ENfO/JcokqP8bZVppC6t4aVwFGu9j/3MXpwp9Jv+qUIuIgPsmtMLnDKZZBsDLQpU/hotgBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T03:41:23.486455Z"},"content_sha256":"029168b3dd5adb10c14b4a5073e198cae0b97fc642597da91755a8395edd4b9e","schema_version":"1.0","event_id":"sha256:029168b3dd5adb10c14b4a5073e198cae0b97fc642597da91755a8395edd4b9e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7YITCQXKGI7QVUVKNFHBW6ESGM/bundle.json","state_url":"https://pith.science/pith/7YITCQXKGI7QVUVKNFHBW6ESGM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7YITCQXKGI7QVUVKNFHBW6ESGM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T03:41:23Z","links":{"resolver":"https://pith.science/pith/7YITCQXKGI7QVUVKNFHBW6ESGM","bundle":"https://pith.science/pith/7YITCQXKGI7QVUVKNFHBW6ESGM/bundle.json","state":"https://pith.science/pith/7YITCQXKGI7QVUVKNFHBW6ESGM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7YITCQXKGI7QVUVKNFHBW6ESGM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7YITCQXKGI7QVUVKNFHBW6ESGM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"95c9ad42e1dd49e428aeff6891f77a73f1da7fb0b48f30b2094c7fc17f527ce8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DS","submitted_at":"2021-12-04T04:07:27Z","title_canon_sha256":"a3d2b46c376be60dd41dbef8ba6cfe199e32710461062d4d2b4b13b7d865b845"},"schema_version":"1.0","source":{"id":"2112.02234","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.02234","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"arxiv_version","alias_value":"2112.02234v1","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02234","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"pith_short_12","alias_value":"7YITCQXKGI7Q","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"pith_short_16","alias_value":"7YITCQXKGI7QVUVK","created_at":"2026-07-05T03:37:50Z"},{"alias_kind":"pith_short_8","alias_value":"7YITCQXK","created_at":"2026-07-05T03:37:50Z"}],"graph_snapshots":[{"event_id":"sha256:029168b3dd5adb10c14b4a5073e198cae0b97fc642597da91755a8395edd4b9e","target":"graph","created_at":"2026-07-05T03:37:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2112.02234/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The $k$-nearest neighbor graph (KNNG) on high-dimensional data is a data structure widely used in many applications such as similarity search, dimension reduction and clustering. Due to its increasing popularity, several methods under the same framework have been proposed in the past decade. This framework contains two steps, i.e. building an initial KNNG (denoted as \\texttt{INIT}) and then refining it by neighborhood propagation (denoted as \\texttt{NBPG}). However, there remain several questions to be answered. First, it lacks a comprehensive experimental comparison among representative solut","authors_text":"Cheng Hong, Cui Jiangtao, Liu Yingfan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DS","submitted_at":"2021-12-04T04:07:27Z","title":"Revisiting $k$-Nearest Neighbor Graph Construction on High-Dimensional Data : Experiments and Analyses"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02234","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7e03b8556147ab128dc4373ddb35224cf96606bd998dfc77d85e95431a4c56eb","target":"record","created_at":"2026-07-05T03:37:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"95c9ad42e1dd49e428aeff6891f77a73f1da7fb0b48f30b2094c7fc17f527ce8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DS","submitted_at":"2021-12-04T04:07:27Z","title_canon_sha256":"a3d2b46c376be60dd41dbef8ba6cfe199e32710461062d4d2b4b13b7d865b845"},"schema_version":"1.0","source":{"id":"2112.02234","kind":"arxiv","version":1}},"canonical_sha256":"fe113142ea323f0ad2aa694e1b78923312a82fc9d7c9a22ae042be924ed622ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe113142ea323f0ad2aa694e1b78923312a82fc9d7c9a22ae042be924ed622ed","first_computed_at":"2026-07-05T03:37:50.249392Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:37:50.249392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7sA5XSLrLbD3LZkUsnv/CLdLBDuB4qftDhz26w3K6OajQS45w3HzZIS0ey7p3L+/ic0fPcy49Xty713FWWBcBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:37:50.249792Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.02234","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7e03b8556147ab128dc4373ddb35224cf96606bd998dfc77d85e95431a4c56eb","sha256:029168b3dd5adb10c14b4a5073e198cae0b97fc642597da91755a8395edd4b9e"],"state_sha256":"68085b14d3bdb3233ff7b631f6b8f7bd9a1ffefb1c6efcc23e3857ec779f25f5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zy+IVqAGUc1CaGr8QzLIsM7dzpiDqXMh/Ptnd4vjakLhk7rA8HW+QPoG8mqgWteNMj1XF3/ry472cvgQmuNHAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T03:41:23.490040Z","bundle_sha256":"052361ce8fbca27afe7f7784e723383e0e46b7135e4bcc2898648c7df178e929"}}