{"paper":{"title":"A likelihood-ratio type test for stochastic block models with bounded degrees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Mingao Yuan, Yang Feng, Zuofeng Shang","submitted_at":"2018-07-12T05:05:09Z","abstract_excerpt":"A fundamental problem in network data analysis is to test Erd\\\"{o}s-R\\'{e}nyi model $\\mathcal{G}\\left(n,\\frac{a+b}{2n}\\right)$ versus a bisection stochastic block model $\\mathcal{G}\\left(n,\\frac{a}{n},\\frac{b}{n}\\right)$, where $a,b>0$ are constants that represent the expected degrees of the graphs and $n$ denotes the number of nodes. This problem serves as the foundation of many other problems such as testing-based methods for determining the number of communities (\\cite{BS16,L16}) and community detection (\\cite{MS16}). Existing work has been focusing on growing-degree regime $a,b\\to\\infty$ ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04426","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"}