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..
Learning Graph Neural Networks with Approximate Gradient Descent
Authors: Qunwei Li, Shaofeng Zou, Wenliang Zhong8438-8446
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are further provided to validate our theoretical analysis. Experimental Results We provide numerical experiments to support and validate our theoretical analysis. |
| Researcher Affiliation | Collaboration | Qunwei Li,1 Shaofeng Zou, 2 Wenliang Zhong 1 1 Ant Group, Hangzhou, China 2 University at Buffalo, the State University of New York |
| Pseudocode | Yes | Algorithm 1: Approximate Gradient Descent for Learning GNNs |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | We assume that the node feature matrix H Rn d is generated independently from the standard Gaussian distribution, and the corresponding output y Rn is generated from the teacher network with true parameters W and v as follows. We assume that each node feature matrix Hj Rnj d is generated independently from the standard Gaussian distribution, and the corresponding output yj R is generated from the teacher network with true parameters W and v as follows. We generate W from unit sphere with a normalized Gaussian matrix, and generate v as a standard Gaussian vector. The nodes in the graphs are probabilistically connected according to the distribution of Bernoulli(0.5). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We choose d = 2 and dout = 1, and set the variance ν to 0.04. The learning rate α is chosen as 0.1. The learning rate α is chosen as 0.005. |