{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:6J2OZRSGCVGFBYSEE2CWQQQYJV","short_pith_number":"pith:6J2OZRSG","schema_version":"1.0","canonical_sha256":"f274ecc646154c50e24426856842184d7562083c170962cb1da06ad6540392b6","source":{"kind":"arxiv","id":"1505.00693","version":1},"attestation_state":"computed","paper":{"title":"n-Level Hypergraph Partitioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Christian Schulz, Henning Meyerhenke, Peter Sanders, Sebastian Schlag, Vitali Henne","submitted_at":"2015-05-04T16:13:11Z","abstract_excerpt":"We develop a multilevel algorithm for hypergraph partitioning that contracts the vertices one at a time and thus allows very high quality. This includes a rating function that avoids nonuniform vertex weights, an efficient \"semi-dynamic\" hypergraph data structure, a very fast coarsening algorithm, and two new local search algorithms. One is a $k$-way hypergraph adaptation of Fiduccia-Mattheyses local search and gives high quality at reasonable cost. The other is an adaptation of size-constrained label propagation to hypergraphs. Comparisons with hMetis and PaToH indicate that the new algorithm"},"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":"1505.00693","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2015-05-04T16:13:11Z","cross_cats_sorted":[],"title_canon_sha256":"53cd5cf2a3475efba2b6c732cae0453deaee71733d4235891109a5e11f934cb2","abstract_canon_sha256":"74ee0d89473028fda79203bd5a28ba6354088c3d6d67a7801c92e0f26566ccac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:02.086048Z","signature_b64":"HJHuOdWsiolEBDFicTY3B3sRXIxhlbsjYrKWcxy4k0evp3DmsPkLvBz/P4Q2q3kTMPlU5Cun395srRLSARRdBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f274ecc646154c50e24426856842184d7562083c170962cb1da06ad6540392b6","last_reissued_at":"2026-05-18T02:17:02.085338Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:02.085338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"n-Level Hypergraph Partitioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Christian Schulz, Henning Meyerhenke, Peter Sanders, Sebastian Schlag, Vitali Henne","submitted_at":"2015-05-04T16:13:11Z","abstract_excerpt":"We develop a multilevel algorithm for hypergraph partitioning that contracts the vertices one at a time and thus allows very high quality. This includes a rating function that avoids nonuniform vertex weights, an efficient \"semi-dynamic\" hypergraph data structure, a very fast coarsening algorithm, and two new local search algorithms. One is a $k$-way hypergraph adaptation of Fiduccia-Mattheyses local search and gives high quality at reasonable cost. The other is an adaptation of size-constrained label propagation to hypergraphs. Comparisons with hMetis and PaToH indicate that the new algorithm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.00693","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":""},"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":"1505.00693","created_at":"2026-05-18T02:17:02.085479+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.00693v1","created_at":"2026-05-18T02:17:02.085479+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.00693","created_at":"2026-05-18T02:17:02.085479+00:00"},{"alias_kind":"pith_short_12","alias_value":"6J2OZRSGCVGF","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_16","alias_value":"6J2OZRSGCVGFBYSE","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_8","alias_value":"6J2OZRSG","created_at":"2026-05-18T12:29:07.941421+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/6J2OZRSGCVGFBYSEE2CWQQQYJV","json":"https://pith.science/pith/6J2OZRSGCVGFBYSEE2CWQQQYJV.json","graph_json":"https://pith.science/api/pith-number/6J2OZRSGCVGFBYSEE2CWQQQYJV/graph.json","events_json":"https://pith.science/api/pith-number/6J2OZRSGCVGFBYSEE2CWQQQYJV/events.json","paper":"https://pith.science/paper/6J2OZRSG"},"agent_actions":{"view_html":"https://pith.science/pith/6J2OZRSGCVGFBYSEE2CWQQQYJV","download_json":"https://pith.science/pith/6J2OZRSGCVGFBYSEE2CWQQQYJV.json","view_paper":"https://pith.science/paper/6J2OZRSG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.00693&json=true","fetch_graph":"https://pith.science/api/pith-number/6J2OZRSGCVGFBYSEE2CWQQQYJV/graph.json","fetch_events":"https://pith.science/api/pith-number/6J2OZRSGCVGFBYSEE2CWQQQYJV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6J2OZRSGCVGFBYSEE2CWQQQYJV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6J2OZRSGCVGFBYSEE2CWQQQYJV/action/storage_attestation","attest_author":"https://pith.science/pith/6J2OZRSGCVGFBYSEE2CWQQQYJV/action/author_attestation","sign_citation":"https://pith.science/pith/6J2OZRSGCVGFBYSEE2CWQQQYJV/action/citation_signature","submit_replication":"https://pith.science/pith/6J2OZRSGCVGFBYSEE2CWQQQYJV/action/replication_record"}},"created_at":"2026-05-18T02:17:02.085479+00:00","updated_at":"2026-05-18T02:17:02.085479+00:00"}