{"paper":{"title":"Multi-Scale Matrix Sampling and Sublinear-Time PageRank Computation","license":"http://creativecommons.org/licenses/publicdomain/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.DS","authors_text":"Christian Borgs, Jennifer Chayes, Michael Brautbar, Shang-Hua Teng","submitted_at":"2012-02-13T15:49:20Z","abstract_excerpt":"A fundamental problem arising in many applications in Web science and social network analysis is, given an arbitrary approximation factor $c>1$, to output a set $S$ of nodes that with high probability contains all nodes of PageRank at least $\\Delta$, and no node of PageRank smaller than $\\Delta/c$. We call this problem {\\sc SignificantPageRanks}. We develop a nearly optimal, local algorithm for the problem with runtime complexity $\\tilde{O}(n/\\Delta)$ on networks with $n$ nodes. We show that any algorithm for solving this problem must have runtime of ${\\Omega}(n/\\Delta)$, rendering our algorit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1202.2771","kind":"arxiv","version":5},"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"}