On the Power of SVD in the Stochastic Block Model
Authors: Xinyu Mao, Jiapeng Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 xinyumao@usc.edu Jiapeng Zhang University of Southern California jiapengz@usc.edu |
| 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. |