Learning Graph Neural Networks with Approximate Gradient Descent
Authors: Qunwei Li, Shaofeng Zou, Wenliang Zhong8438-8446
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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. |