On the Power of Louvain in the Stochastic Block Model
Authors: Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluated the performances of Louvain in the SBM. |
| Researcher Affiliation | Collaboration | 1Google Z urich, Switzerland 2Nav Algo, France 3Sorbonne Universit e, UPMC Univ Paris 06, CNRS, LIP6, France 4King s College London, UK |
| Pseudocode | No | The paper describes algorithms but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | Our implementations builds on the Louvain implementation of Guillaume [23]. (Reference [23]: Jean-Loup Guillaume. https://github.com/jlguillaume/louvain, 2020. [Online; accessed 29-may-2020]). The paper builds on an existing implementation but does not state that its own source code for the methodology is released. |
| Open Datasets | No | The paper uses the Stochastic Block Model (SBM) to generate graphs, which is a generative model rather than a fixed, publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits, as it focuses on theoretical analysis and experiments on graphs generated by a model, not on fixed datasets with pre-defined splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper mentions building on 'the Louvain implementation of Guillaume [23]' but does not specify version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | In our experiments, we set q = p/2. |