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 [1].
Strong Consistency, Graph Laplacians, and the Stochastic Block Model
Authors: Shaofeng Deng, Shuyang Ling, Thomas Strohmer
JMLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are given in Section 4 which complement our theoretical analysis. ... We illustrate the strong consistency of both spectral clustering methods in Figure 2. It can be clearly seen that both methods achieve strong consistency down to the theoretical threshold α β > 2. |
| Researcher Affiliation | Academia | Shaofeng Deng EMAIL Department of Mathematics University of California at Davis Davis, CA 95616, USA; Shuyang Ling EMAIL New York University Shanghai Shanghai, 200122, China; Thomas Strohmer EMAIL Center of Data Science and Artificial Intelligence Research Department of Mathematics University of California at Davis Davis, CA 95616, USA |
| Pseudocode | Yes | Algorithm 1 Unnormalized spectral clustering ... Algorithm 2 Normalized spectral clustering |
| Open Source Code | No | No explicit mention or link to source code is provided in the paper. |
| Open Datasets | No | The paper uses the stochastic block model (SBM) to generate synthetic data for its analysis and numerical explorations, rather than using a pre-existing, publicly available dataset. |
| Dataset Splits | No | The paper generates data using the stochastic block model (SBM) for its numerical experiments and does not use or specify splits for a publicly available dataset. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper describes the methods but does not list any specific software libraries or tools with version numbers used for implementation. |
| Experiment Setup | Yes | We fix n = 600 and the number of trials to be 20. For each pair of α and β, we run both methods and count how many times each method succeeds. ... We fix n = 5000, α = 10, β = 2 and the number of trials as 100. |