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..
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
Authors: Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we provide numerical experiments to demonstrate the validity of our analysis and the effectiveness of the proposed learning algorithm for GNNs. [...] Section 5 shows the numerical results |
| Researcher Affiliation | Collaboration | 1Dept. of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, NY, USA 2MIT-IBM Waston AI Lab, Cambridge, MA, USA 3IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. |
| Pseudocode | Yes | Algorithm 1 Accelerated Gradient Descent Algorithm with Tensor Initialization |
| Open Source Code | No | The paper does not provide any links or explicit statements about releasing open-source code for the described methodology. |
| Open Datasets | No | We verify our results on synthetic graph-structured data. [...] The feature vectors {xn}N n=1 are randomly generated from the standard Gaussian distribution N(0, Id d). |
| Dataset Splits | No | The paper mentions "Partition Ωinto T = log(1/ε) disjoint subsets, denoted as {Ωt}T t=1;" for the algorithm, but does not specify a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Input: X, yn n Ω, A, the step size η, the momentum constant β, and the error tolerance ε; [...] The dimension d of the feature vectors is chosen as 10, and the sample size |Ω| is chosen as 2000. [...] The initialization is randomly selected from W (0) W (0) W F / W F < 0.5 to reduce the computation. |