{"paper":{"title":"On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Eric D. Kolaczyk, Prakash Balachandran, Weston Viles","submitted_at":"2014-09-19T13:10:02Z","abstract_excerpt":"Our work in this paper is inspired by a statistical observation that is both elementary and broadly relevant to network analysis in practice -- that the uncertainty in approximating some true network graph $G=(V,E)$ by some estimated graph $\\hat{G}=(V,\\hat{E})$ manifests as errors in the status of (non)edges that must necessarily propagate to any estimates of network summaries $\\eta(G)$ we seek. Motivated by the common practice of using plug-in estimates $\\eta(\\hat{G})$ as proxies for $\\eta(G)$, our focus is on the problem of characterizing the distribution of the discrepancy $D=\\eta(\\hat{G}) "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.5640","kind":"arxiv","version":3},"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"}