{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:ECZL2FU5NGB7HC3LSPTIJGPHHX","short_pith_number":"pith:ECZL2FU5","schema_version":"1.0","canonical_sha256":"20b2bd169d6983f38b6b93e68499e73dd297f12bcfd14431c47d869ee9d3beb8","source":{"kind":"arxiv","id":"1305.5520","version":2},"attestation_state":"computed","paper":{"title":"Distributed Minimum Cut Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.DS","authors_text":"Fabian Kuhn, Mohsen Ghaffari","submitted_at":"2013-05-23T19:13:15Z","abstract_excerpt":"We study the problem of computing approximate minimum edge cuts by distributed algorithms. We use a standard synchronous message passing model where in each round, $O(\\log n)$ bits can be transmitted over each edge (a.k.a. the CONGEST model). We present a distributed algorithm that, for any weighted graph and any $\\epsilon \\in (0, 1)$, with high probability finds a cut of size at most $O(\\epsilon^{-1}\\lambda)$ in $O(D) + \\tilde{O}(n^{1/2 + \\epsilon})$ rounds, where $\\lambda$ is the size of the minimum cut. This algorithm is based on a simple approach for analyzing random edge sampling, which w"},"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":"1305.5520","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2013-05-23T19:13:15Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"5831f9c15e9dff06ceb41cbfb65fb9fb0f4002e7ad0cda569f785453e150f5ee","abstract_canon_sha256":"e654d67d39b5200a9c08b365031de2223ce90e83e8cfd6985ff883ab3fb029ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:06:35.177834Z","signature_b64":"PwDHI5vrfYPc+68tGEKv4atXrUvaJhB7Zs19d5AISNqZLKCTHEz8/ihltbdUixMKxnR7cGrk47MHBUWxA6iWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"20b2bd169d6983f38b6b93e68499e73dd297f12bcfd14431c47d869ee9d3beb8","last_reissued_at":"2026-05-18T03:06:35.177215Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:06:35.177215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed Minimum Cut Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.DS","authors_text":"Fabian Kuhn, Mohsen Ghaffari","submitted_at":"2013-05-23T19:13:15Z","abstract_excerpt":"We study the problem of computing approximate minimum edge cuts by distributed algorithms. We use a standard synchronous message passing model where in each round, $O(\\log n)$ bits can be transmitted over each edge (a.k.a. the CONGEST model). We present a distributed algorithm that, for any weighted graph and any $\\epsilon \\in (0, 1)$, with high probability finds a cut of size at most $O(\\epsilon^{-1}\\lambda)$ in $O(D) + \\tilde{O}(n^{1/2 + \\epsilon})$ rounds, where $\\lambda$ is the size of the minimum cut. This algorithm is based on a simple approach for analyzing random edge sampling, which w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1305.5520","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":"1305.5520","created_at":"2026-05-18T03:06:35.177299+00:00"},{"alias_kind":"arxiv_version","alias_value":"1305.5520v2","created_at":"2026-05-18T03:06:35.177299+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1305.5520","created_at":"2026-05-18T03:06:35.177299+00:00"},{"alias_kind":"pith_short_12","alias_value":"ECZL2FU5NGB7","created_at":"2026-05-18T12:27:43.054852+00:00"},{"alias_kind":"pith_short_16","alias_value":"ECZL2FU5NGB7HC3L","created_at":"2026-05-18T12:27:43.054852+00:00"},{"alias_kind":"pith_short_8","alias_value":"ECZL2FU5","created_at":"2026-05-18T12:27:43.054852+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/ECZL2FU5NGB7HC3LSPTIJGPHHX","json":"https://pith.science/pith/ECZL2FU5NGB7HC3LSPTIJGPHHX.json","graph_json":"https://pith.science/api/pith-number/ECZL2FU5NGB7HC3LSPTIJGPHHX/graph.json","events_json":"https://pith.science/api/pith-number/ECZL2FU5NGB7HC3LSPTIJGPHHX/events.json","paper":"https://pith.science/paper/ECZL2FU5"},"agent_actions":{"view_html":"https://pith.science/pith/ECZL2FU5NGB7HC3LSPTIJGPHHX","download_json":"https://pith.science/pith/ECZL2FU5NGB7HC3LSPTIJGPHHX.json","view_paper":"https://pith.science/paper/ECZL2FU5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1305.5520&json=true","fetch_graph":"https://pith.science/api/pith-number/ECZL2FU5NGB7HC3LSPTIJGPHHX/graph.json","fetch_events":"https://pith.science/api/pith-number/ECZL2FU5NGB7HC3LSPTIJGPHHX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ECZL2FU5NGB7HC3LSPTIJGPHHX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ECZL2FU5NGB7HC3LSPTIJGPHHX/action/storage_attestation","attest_author":"https://pith.science/pith/ECZL2FU5NGB7HC3LSPTIJGPHHX/action/author_attestation","sign_citation":"https://pith.science/pith/ECZL2FU5NGB7HC3LSPTIJGPHHX/action/citation_signature","submit_replication":"https://pith.science/pith/ECZL2FU5NGB7HC3LSPTIJGPHHX/action/replication_record"}},"created_at":"2026-05-18T03:06:35.177299+00:00","updated_at":"2026-05-18T03:06:35.177299+00:00"}