Optimal Cluster Recovery in the Labeled Stochastic Block Model
Authors: Se-Young Yun, Alexandre Proutiere
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We find the set of parameters such that there exists a clustering algorithm with at most s misclassified items in average under the general LSBM and for any s = o(n), which solves one open problem raised in [2]. We further develop an algorithm, based on simple spectral methods, that achieves this fundamental performance limit within O(npolylog(n)) computations and without the a-priori knowledge of the model parameters. |
| Researcher Affiliation | Collaboration | Se-Young Yun CNLS, Los Alamos National Lab. Los Alamos, NM 87545 syun@lanl.gov Alexandre Proutiere Automatic Control Dept., KTH Stockholm 100-44, Sweden alepro@kth.se |
| Pseudocode | Yes | The SP algorithm consists in two parts, and its detailed pseudo-code is presented at the beginning of the supplementary document (see Algorithm 1). |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical models (Labeled Stochastic Block Model) rather than empirical evaluation on real-world datasets. No datasets, public or otherwise, are mentioned. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm complexity, not empirical execution. Therefore, it does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers required for reproducibility. |
| Experiment Setup | No | The paper is theoretical, presenting an algorithm and its mathematical analysis, rather than empirical experiments. Therefore, it does not include details on an experimental setup, hyperparameters, or training configurations. |