{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DH53XB2DBFXTGHY2J7NXGMRKJU","short_pith_number":"pith:DH53XB2D","schema_version":"1.0","canonical_sha256":"19fbbb8743096f331f1a4fdb73322a4d3cb30d480dd039167620942a3a9d3811","source":{"kind":"arxiv","id":"2312.04876","version":6},"attestation_state":"computed","paper":{"title":"GVE-Louvain: Fast Louvain Algorithm for Community Detection in Shared Memory Setting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.DC","authors_text":"Subhajit Sahu","submitted_at":"2023-12-08T07:26:24Z","abstract_excerpt":"Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This technical report presents one of the most efficient multicore implementations of the Louvain algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Louvain, which we term as GVE-Louvain, outperforms Vite, Grappolo, NetworKit Louvain, and cuGraph Louvain (running on NVIDIA A10"},"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":"2312.04876","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2023-12-08T07:26:24Z","cross_cats_sorted":["cs.PF"],"title_canon_sha256":"c7533ecea963f5afaf656abaab05e625821118447b74db9428d250b26d20816a","abstract_canon_sha256":"31f50db459a5b5d9815326bd06e42ca5fc1345eb57f32581f9409ea92a46c611"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:00.006289Z","signature_b64":"WZOCHyKSIiCgjWupeZGD157rkCqgHBz7DFlnBd6RTCj8lMR70udcen0MLvPHW+5+qEjEdKqU9S2/gYFZ9iWNCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19fbbb8743096f331f1a4fdb73322a4d3cb30d480dd039167620942a3a9d3811","last_reissued_at":"2026-07-05T11:25:00.005933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:00.005933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GVE-Louvain: Fast Louvain Algorithm for Community Detection in Shared Memory Setting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.DC","authors_text":"Subhajit Sahu","submitted_at":"2023-12-08T07:26:24Z","abstract_excerpt":"Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This technical report presents one of the most efficient multicore implementations of the Louvain algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Louvain, which we term as GVE-Louvain, outperforms Vite, Grappolo, NetworKit Louvain, and cuGraph Louvain (running on NVIDIA A10"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.04876","kind":"arxiv","version":6},"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/2312.04876/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":"2312.04876","created_at":"2026-07-05T11:25:00.005989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.04876v6","created_at":"2026-07-05T11:25:00.005989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.04876","created_at":"2026-07-05T11:25:00.005989+00:00"},{"alias_kind":"pith_short_12","alias_value":"DH53XB2DBFXT","created_at":"2026-07-05T11:25:00.005989+00:00"},{"alias_kind":"pith_short_16","alias_value":"DH53XB2DBFXTGHY2","created_at":"2026-07-05T11:25:00.005989+00:00"},{"alias_kind":"pith_short_8","alias_value":"DH53XB2D","created_at":"2026-07-05T11:25:00.005989+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.19004","citing_title":"CPU vs. GPU for Community Detection: Performance Insights from GVE-Louvain and $\\nu$-Louvain","ref_index":45,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU","json":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU.json","graph_json":"https://pith.science/api/pith-number/DH53XB2DBFXTGHY2J7NXGMRKJU/graph.json","events_json":"https://pith.science/api/pith-number/DH53XB2DBFXTGHY2J7NXGMRKJU/events.json","paper":"https://pith.science/paper/DH53XB2D"},"agent_actions":{"view_html":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU","download_json":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU.json","view_paper":"https://pith.science/paper/DH53XB2D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.04876&json=true","fetch_graph":"https://pith.science/api/pith-number/DH53XB2DBFXTGHY2J7NXGMRKJU/graph.json","fetch_events":"https://pith.science/api/pith-number/DH53XB2DBFXTGHY2J7NXGMRKJU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU/action/storage_attestation","attest_author":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU/action/author_attestation","sign_citation":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU/action/citation_signature","submit_replication":"https://pith.science/pith/DH53XB2DBFXTGHY2J7NXGMRKJU/action/replication_record"}},"created_at":"2026-07-05T11:25:00.005989+00:00","updated_at":"2026-07-05T11:25:00.005989+00:00"}