{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:GFZPUYYYMY4QZI23YAWAN3UJOA","short_pith_number":"pith:GFZPUYYY","schema_version":"1.0","canonical_sha256":"3172fa631866390ca35bc02c06ee897020c3ad734fa3fea4edebbf2d82c450ac","source":{"kind":"arxiv","id":"1710.04584","version":4},"attestation_state":"computed","paper":{"title":"Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Yongyu Wang, Zhuo Feng","submitted_at":"2017-10-12T16:09:29Z","abstract_excerpt":"The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering. In this work, we introduce a highly-scalable, spectrum-preserving graph sparsification algorithm that enables to build ultra-sparse NN (u-NN) graphs with guaranteed preservation of the original graph spectrums, such as the first few eigenvectors of the original graph Laplacian. Our approach can immediately lead to scalable spectral clustering of large data networks without sacrificing solution quality. The proposed method starts from constructing low-stretch span"},"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":"1710.04584","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-12T16:09:29Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"45823a58243a6c4f28b7ef25b5d8a39c6c7d625fbdf7a1d7940eebb876c9721d","abstract_canon_sha256":"894b58110aeb1020bf1d0b17e2793a5d13a0850b060019017678251747c1cec4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:37.218611Z","signature_b64":"e0Txqla0i7qUKibg7Pbx/AXMVCQhHiGuPNPA9aUl4rasp+Po60H4Vh/5+IVvgVltmlOcBVDlDVc12I0eOcXRDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3172fa631866390ca35bc02c06ee897020c3ad734fa3fea4edebbf2d82c450ac","last_reissued_at":"2026-05-18T00:03:37.218061Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:37.218061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Yongyu Wang, Zhuo Feng","submitted_at":"2017-10-12T16:09:29Z","abstract_excerpt":"The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering. In this work, we introduce a highly-scalable, spectrum-preserving graph sparsification algorithm that enables to build ultra-sparse NN (u-NN) graphs with guaranteed preservation of the original graph spectrums, such as the first few eigenvectors of the original graph Laplacian. Our approach can immediately lead to scalable spectral clustering of large data networks without sacrificing solution quality. The proposed method starts from constructing low-stretch span"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04584","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":""},"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":"1710.04584","created_at":"2026-05-18T00:03:37.218152+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04584v4","created_at":"2026-05-18T00:03:37.218152+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04584","created_at":"2026-05-18T00:03:37.218152+00:00"},{"alias_kind":"pith_short_12","alias_value":"GFZPUYYYMY4Q","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"GFZPUYYYMY4QZI23","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"GFZPUYYY","created_at":"2026-05-18T12:31:15.632608+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/GFZPUYYYMY4QZI23YAWAN3UJOA","json":"https://pith.science/pith/GFZPUYYYMY4QZI23YAWAN3UJOA.json","graph_json":"https://pith.science/api/pith-number/GFZPUYYYMY4QZI23YAWAN3UJOA/graph.json","events_json":"https://pith.science/api/pith-number/GFZPUYYYMY4QZI23YAWAN3UJOA/events.json","paper":"https://pith.science/paper/GFZPUYYY"},"agent_actions":{"view_html":"https://pith.science/pith/GFZPUYYYMY4QZI23YAWAN3UJOA","download_json":"https://pith.science/pith/GFZPUYYYMY4QZI23YAWAN3UJOA.json","view_paper":"https://pith.science/paper/GFZPUYYY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04584&json=true","fetch_graph":"https://pith.science/api/pith-number/GFZPUYYYMY4QZI23YAWAN3UJOA/graph.json","fetch_events":"https://pith.science/api/pith-number/GFZPUYYYMY4QZI23YAWAN3UJOA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GFZPUYYYMY4QZI23YAWAN3UJOA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GFZPUYYYMY4QZI23YAWAN3UJOA/action/storage_attestation","attest_author":"https://pith.science/pith/GFZPUYYYMY4QZI23YAWAN3UJOA/action/author_attestation","sign_citation":"https://pith.science/pith/GFZPUYYYMY4QZI23YAWAN3UJOA/action/citation_signature","submit_replication":"https://pith.science/pith/GFZPUYYYMY4QZI23YAWAN3UJOA/action/replication_record"}},"created_at":"2026-05-18T00:03:37.218152+00:00","updated_at":"2026-05-18T00:03:37.218152+00:00"}