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..
A Convergence Analysis of Gradient Descent on Graph Neural Networks
Authors: Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we verify our theoretical results via simulations. |
| Researcher Affiliation | Industry | Pranjal Awasthi Google Research EMAIL Abhimanyu Das Google Research EMAIL Sreenivas Gollapudi Google Research EMAIL |
| Pseudocode | No | Explanation: The paper does not contain any sections or figures explicitly labeled as "Pseudocode" or "Algorithm" presenting structured steps. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See supplementary material. |
| Open Datasets | No | We generate an unknown ground truth network in the case of Re LU GNNs by choosing each column of W to be a random unit length vector. For the case of linear networks we generate positive de๏ฌnite matrices W 1 , W 2 by picking random Gaussian entries, and then adding a small multiplicative factor of 0.001 times the identity matrix. |
| Dataset Splits | No | Explanation: The paper does not specify traditional dataset splits (e.g., training, validation, testing percentages or counts) as it operates in a 'realizable setting' with data generated from an unknown GNN. |
| Hardware Specification | No | Our experiments are run using one GPU. |
| Software Dependencies | No | We simulate population gradient descent and implement our networks using the JAX programming language [Bradbury et al., 2018]. |
| Experiment Setup | Yes | For the case of one round GNNs with Re LU activations we set the embedding size r = 10, and h = 10 (number of hidden units in the Re LU GNN). We use the same value of r for the case of deep linear GNNs, where r equals the input dimensionality and also the dimensionality of the matrices W 1 , and W 2 . |