{"paper":{"title":"Graph Sparsification via Refinement Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Ashish Goel, Michael Kapralov, Sanjeev Khanna","submitted_at":"2010-04-27T21:05:00Z","abstract_excerpt":"A graph G'(V,E') is an \\eps-sparsification of G for some \\eps>0, if every (weighted) cut in G' is within (1\\pm \\eps) of the corresponding cut in G. A celebrated result of Benczur and Karger shows that for every undirected graph G, an \\eps-sparsification with O(n\\log n/\\e^2) edges can be constructed in O(m\\log^2n) time. Applications to modern massive data sets often constrain algorithms to use computation models that restrict random access to the input. The semi-streaming model, in which the algorithm is constrained to use \\tilde O(n) space, has been shown to be a good abstraction for analyzing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1004.4915","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"}