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..
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Authors: Rongzhe Wei, Haoteng YIN, Junteng Jia, Austin R. Benson, Pan Li
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluation on both synthetic and real datasets demonstrates our theory. Specifically, the node mis-classification errors of three citation networks with different levels of attributed information (Gaussian attributes) are shown in Fig. 1, which precisely matches the above conclusions. |
| Researcher Affiliation | Academia | Rongzhe Wei1, Haoteng Yin1, Junteng Jia2, Austin R. Benson2, Pan Li1 1 Department of Computer Science, Purdue University 2 Department of Computer Science, Cornell University |
| Pseudocode | No | The paper does not contain any section explicitly labeled “Pseudocode” or “Algorithm”, nor are there structured code-like blocks. |
| Open Source Code | Yes | Our code is available at https://github. com/Graph-COM/Bayesian_inference_based_GNN.git. |
| Open Datasets | Yes | This experiments compare non-linear models and linear models under Gaussian and Laplacian attributes on three benchmark citation networks Pub Med, Cora, and Cite Seer [92]. |
| Dataset Splits | No | One graph is used for training and the other one for testing. |
| Hardware Specification | No | The provided text does not explicitly describe the hardware used for running the experiments. It only mentions a reference to Appendix G for compute resources, which is not available. |
| Software Dependencies | No | The provided text does not explicitly list any software dependencies with specific version numbers. |
| Experiment Setup | Yes | The model is trained with Adam optimizer (learning rate = 1e-2, weight decay = 5e-4). |