{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7WB3BUG3VOK7GTXV5QW56KZWZB","short_pith_number":"pith:7WB3BUG3","schema_version":"1.0","canonical_sha256":"fd83b0d0dbab95f34ef5ec2ddf2b36c850015f1acec5d0568a6806ae4f27ecaf","source":{"kind":"arxiv","id":"1907.06768","version":2},"attestation_state":"computed","paper":{"title":"Partitioning Graphs for the Cloud using Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Mohammad Hammoud, Mohammad Hasanzadeh Mofrad, Rami Melhem","submitted_at":"2019-07-15T21:50:56Z","abstract_excerpt":"In this paper, we propose Revolver, a parallel graph partitioning algorithm capable of partitioning large-scale graphs on a single shared-memory machine. Revolver employs an asynchronous processing framework, which leverages reinforcement learning and label propagation to adaptively partition a graph. In addition, it adopts a vertex-centric view of the graph where each vertex is assigned an autonomous agent responsible for selecting a suitable partition for it, distributing thereby the computation across all vertices. The intuition behind using a vertex-centric view is that it naturally fits t"},"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":"1907.06768","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-07-15T21:50:56Z","cross_cats_sorted":[],"title_canon_sha256":"0d0d0a487e32174cce5a4c4deea4c67ef2321abfe2f6356a2dcff7bf5502d6a1","abstract_canon_sha256":"a046d957a445f4bfd16623702b1223ec038625fab4d6234a8c6dbd2b63d0d365"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:22.945431Z","signature_b64":"c5dpq6LeRYwChbyaVts7wWwns8VitMpYnhJSGokQ381Qm4zOF2nvwESTo1XkIkIWyrGpsVOAZx9CKzjDq7roAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd83b0d0dbab95f34ef5ec2ddf2b36c850015f1acec5d0568a6806ae4f27ecaf","last_reissued_at":"2026-05-17T23:40:22.944616Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:22.944616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Partitioning Graphs for the Cloud using Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Mohammad Hammoud, Mohammad Hasanzadeh Mofrad, Rami Melhem","submitted_at":"2019-07-15T21:50:56Z","abstract_excerpt":"In this paper, we propose Revolver, a parallel graph partitioning algorithm capable of partitioning large-scale graphs on a single shared-memory machine. Revolver employs an asynchronous processing framework, which leverages reinforcement learning and label propagation to adaptively partition a graph. In addition, it adopts a vertex-centric view of the graph where each vertex is assigned an autonomous agent responsible for selecting a suitable partition for it, distributing thereby the computation across all vertices. The intuition behind using a vertex-centric view is that it naturally fits t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06768","kind":"arxiv","version":2},"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":"1907.06768","created_at":"2026-05-17T23:40:22.944731+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.06768v2","created_at":"2026-05-17T23:40:22.944731+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06768","created_at":"2026-05-17T23:40:22.944731+00:00"},{"alias_kind":"pith_short_12","alias_value":"7WB3BUG3VOK7","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7WB3BUG3VOK7GTXV","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7WB3BUG3","created_at":"2026-05-18T12:33:12.712433+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/7WB3BUG3VOK7GTXV5QW56KZWZB","json":"https://pith.science/pith/7WB3BUG3VOK7GTXV5QW56KZWZB.json","graph_json":"https://pith.science/api/pith-number/7WB3BUG3VOK7GTXV5QW56KZWZB/graph.json","events_json":"https://pith.science/api/pith-number/7WB3BUG3VOK7GTXV5QW56KZWZB/events.json","paper":"https://pith.science/paper/7WB3BUG3"},"agent_actions":{"view_html":"https://pith.science/pith/7WB3BUG3VOK7GTXV5QW56KZWZB","download_json":"https://pith.science/pith/7WB3BUG3VOK7GTXV5QW56KZWZB.json","view_paper":"https://pith.science/paper/7WB3BUG3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.06768&json=true","fetch_graph":"https://pith.science/api/pith-number/7WB3BUG3VOK7GTXV5QW56KZWZB/graph.json","fetch_events":"https://pith.science/api/pith-number/7WB3BUG3VOK7GTXV5QW56KZWZB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7WB3BUG3VOK7GTXV5QW56KZWZB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7WB3BUG3VOK7GTXV5QW56KZWZB/action/storage_attestation","attest_author":"https://pith.science/pith/7WB3BUG3VOK7GTXV5QW56KZWZB/action/author_attestation","sign_citation":"https://pith.science/pith/7WB3BUG3VOK7GTXV5QW56KZWZB/action/citation_signature","submit_replication":"https://pith.science/pith/7WB3BUG3VOK7GTXV5QW56KZWZB/action/replication_record"}},"created_at":"2026-05-17T23:40:22.944731+00:00","updated_at":"2026-05-17T23:40:22.944731+00:00"}