Research map / Learning theory and algorithms / Sample complexity
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Query-Limited Community Recovery in Stochastic Block Models (NeurIPS 2026 · similarity 0.89)
- Network two-sample test for block models (NeurIPS 2025 · similarity 0.88)
- Differentially private exact recovery for stochastic block models (ICML 2024 · similarity 0.88)
- Efficient Graph Matching for Correlated Stochastic Block Models (NeurIPS 2024 · similarity 0.87)
- Exact Community Recovery under Side Information: Optimality of Spectral Algorithms (ICLR 2025 · similarity 0.87)
- Harnessing Multiple Correlated Networks for Exact Community Recovery (NeurIPS 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.