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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Universality of Graph Neural Networks on Large Random Graphs
Authors: Nicolas Keriven, Alberto Bietti, Samuel Vaiter
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The code for the numerical illustrations is available at https://github.com/ nkeriven/random-graph-gnn. Figure 3: Illustration of Prop. 9 on Gaussian kernel, but with random edges and two-hop input ๏ฌltering. The x-axis is the latent variables in X = [ 1, 1]. The y-axis is the output of a SGNN trained to approximate some function f (red curve). |
| Researcher Affiliation | Academia | Nicolas Keriven CNRS, GIPSA-lab, Grenoble, France EMAIL Alberto Bietti NYU Center for Data Science, New York, USA EMAIL Samuel Vaiter CNRS, LJAD, Nice, France EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for the numerical illustrations is available at https://github.com/ nkeriven/random-graph-gnn. |
| Open Datasets | No | The paper discusses theoretical models of random graphs like SBMs and radial kernels, but does not specify a publicly available named dataset for training purposes. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., train/validation/test percentages or counts) needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, such as concrete hyperparameter values or training configurations, in the main text. |