{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:S55C2BWYEBAO7DPXIXWGWJ3GF3","short_pith_number":"pith:S55C2BWY","schema_version":"1.0","canonical_sha256":"977a2d06d82040ef8df745ec6b27662ef827505b7c42f8430c6f9f45b43ea570","source":{"kind":"arxiv","id":"1205.2234","version":1},"attestation_state":"computed","paper":{"title":"Approximation Algorithms for Semi-random Graph Partitioning Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC"],"primary_cat":"cs.DS","authors_text":"Aravindan Vijayaraghavan, Konstantin Makarychev, Yury Makarychev","submitted_at":"2012-05-10T11:22:24Z","abstract_excerpt":"In this paper, we propose and study a new semi-random model for graph partitioning problems. We believe that it captures many properties of real--world instances. The model is more flexible than the semi-random model of Feige and Kilian and planted random model of Bui, Chaudhuri, Leighton and Sipser.\n  We develop a general framework for solving semi-random instances and apply it to several problems of interest. We present constant factor bi-criteria approximation algorithms for semi-random instances of the Balanced Cut, Multicut, Min Uncut, Sparsest Cut and Small Set Expansion problems. We als"},"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":"1205.2234","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2012-05-10T11:22:24Z","cross_cats_sorted":["cs.CC"],"title_canon_sha256":"7a8037b988e56cffaf85a798b08a9bfc297acb2be1e4d2689b45290665c7510e","abstract_canon_sha256":"2a622c20b80dca28a9d1bad4d7b1c338aead9de2739508dd54b140c39484129f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:21:10.461217Z","signature_b64":"rhvYQ9t5vuN9trmGpiZbHiziZ4hO6pPYyLa+og7VaQvNn9Df7kTc1frKWsGMrhb/Che6x/gTUBwouBCZmj7RDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"977a2d06d82040ef8df745ec6b27662ef827505b7c42f8430c6f9f45b43ea570","last_reissued_at":"2026-05-18T02:21:10.460717Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:21:10.460717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximation Algorithms for Semi-random Graph Partitioning Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC"],"primary_cat":"cs.DS","authors_text":"Aravindan Vijayaraghavan, Konstantin Makarychev, Yury Makarychev","submitted_at":"2012-05-10T11:22:24Z","abstract_excerpt":"In this paper, we propose and study a new semi-random model for graph partitioning problems. We believe that it captures many properties of real--world instances. The model is more flexible than the semi-random model of Feige and Kilian and planted random model of Bui, Chaudhuri, Leighton and Sipser.\n  We develop a general framework for solving semi-random instances and apply it to several problems of interest. We present constant factor bi-criteria approximation algorithms for semi-random instances of the Balanced Cut, Multicut, Min Uncut, Sparsest Cut and Small Set Expansion problems. We als"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1205.2234","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":"1205.2234","created_at":"2026-05-18T02:21:10.460792+00:00"},{"alias_kind":"arxiv_version","alias_value":"1205.2234v1","created_at":"2026-05-18T02:21:10.460792+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1205.2234","created_at":"2026-05-18T02:21:10.460792+00:00"},{"alias_kind":"pith_short_12","alias_value":"S55C2BWYEBAO","created_at":"2026-05-18T12:27:20.899486+00:00"},{"alias_kind":"pith_short_16","alias_value":"S55C2BWYEBAO7DPX","created_at":"2026-05-18T12:27:20.899486+00:00"},{"alias_kind":"pith_short_8","alias_value":"S55C2BWY","created_at":"2026-05-18T12:27:20.899486+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/S55C2BWYEBAO7DPXIXWGWJ3GF3","json":"https://pith.science/pith/S55C2BWYEBAO7DPXIXWGWJ3GF3.json","graph_json":"https://pith.science/api/pith-number/S55C2BWYEBAO7DPXIXWGWJ3GF3/graph.json","events_json":"https://pith.science/api/pith-number/S55C2BWYEBAO7DPXIXWGWJ3GF3/events.json","paper":"https://pith.science/paper/S55C2BWY"},"agent_actions":{"view_html":"https://pith.science/pith/S55C2BWYEBAO7DPXIXWGWJ3GF3","download_json":"https://pith.science/pith/S55C2BWYEBAO7DPXIXWGWJ3GF3.json","view_paper":"https://pith.science/paper/S55C2BWY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1205.2234&json=true","fetch_graph":"https://pith.science/api/pith-number/S55C2BWYEBAO7DPXIXWGWJ3GF3/graph.json","fetch_events":"https://pith.science/api/pith-number/S55C2BWYEBAO7DPXIXWGWJ3GF3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S55C2BWYEBAO7DPXIXWGWJ3GF3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S55C2BWYEBAO7DPXIXWGWJ3GF3/action/storage_attestation","attest_author":"https://pith.science/pith/S55C2BWYEBAO7DPXIXWGWJ3GF3/action/author_attestation","sign_citation":"https://pith.science/pith/S55C2BWYEBAO7DPXIXWGWJ3GF3/action/citation_signature","submit_replication":"https://pith.science/pith/S55C2BWYEBAO7DPXIXWGWJ3GF3/action/replication_record"}},"created_at":"2026-05-18T02:21:10.460792+00:00","updated_at":"2026-05-18T02:21:10.460792+00:00"}