Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Power of SVD in the Stochastic Block Model
Authors: Xinyu Mao, Jiapeng Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the power of vanilla-SVD algorithm in the stochastic block model (SBM). We show that, in the symmetric setting, vanilla-SVD algorithm recovers all clusters correctly. This result answers an open question posed by Van Vu (Combinatorics Probability and Computing, 2018) in the symmetric setting. |
| Researcher Affiliation | Academia | Xinyu Mao University of Southern California EMAIL Jiapeng Zhang University of Southern California EMAIL |
| Pseudocode | Yes | Algorithm 1: Vanilla-SVD algorithm for graph clustering |
| Open Source Code | No | The paper does not include any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper focuses on theoretical analysis of the Stochastic Block Model (SBM) and does not describe experiments involving training on datasets. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe experiments involving validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe an experimental setup with hyperparameters or system-level training settings. |