Latent distance estimation for random geometric graphs
Authors: Ernesto Araya Valdivia, De Castro Yohann
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop an efficient algorithm, which we call Harmonic Eigen Cluster(HEi C) to reconstruct the latent positions from the data and illustrate its usefulness with synthetic data.We generate synthetic data using different geometric graphons. |
| Researcher Affiliation | Academia | Ernesto Araya Laboratoire de Mathématiques d Orsay (LMO) Université Paris-Sud 91405 Orsay Cedex, France ernesto.araya-valdivia@u-psud.fr Yohann De Castro Institut Camille Jordan École Centrale de Lyon 69134 Écully, France yohann.de-castro@ec-lyon.fr |
| Pseudocode | Yes | Algorithm 1: Harmonic Eigen Cluster(HEi C) algorithm |
| Open Source Code | No | No explicit statement or link to open-source code for the described methodology was found. |
| Open Datasets | No | The paper uses synthetic data generated for the experiments and does not provide concrete access information (link, DOI, formal citation) for a publicly available dataset. |
| Dataset Splits | No | The paper describes generating synthetic data and running simulations but does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | Yes | The experiments were performed on a 3,3Ghz Intel i5 with 16GB RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | No | The paper describes parameters for data generation (e.g., 'sample 100 Gram matrices', 'sampling 1000 point', 'repeating the procedure 50 times') but does not specify experimental setup details like hyperparameters (e.g., learning rate, batch size, optimizer settings) for a model's training. |