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 Metadata-Driven Approach to Understand Graph Neural Networks
Authors: Ting Wei Li, Qiaozhu Mei, Jiaqi Ma
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a multivariate sparse regression analysis on the metadata derived from benchmarking GNN performance across diverse datasets, yielding a set of salient data properties. To validate the effectiveness of our data-driven approach, we focus on one identified data property, the degree distribution, and investigate how this property influences GNN performance through theoretical analysis and controlled experiments. |
| Researcher Affiliation | Academia | Ting Wei Li University of Michigan EMAIL Qiaozhu Mei University of Michigan EMAIL Jiaqi Ma University of Illinois Urbana-Champaign EMAIL |
| Pseudocode | No | The paper describes methods and processes in text and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the authors have made the source code for their proposed methodology publicly available. |
| Open Datasets | Yes | We obtain both the benchmark datasets and the model performance using the Graph Learning Indexer (GLI) library [27]. We include the following benchmark datasets in our regression analysis: cora [42], citeseer [42], pubmed [42], texas [33], cornell [33], wisconsin [33], actor [33], squirrel [33], chameleon [33], arxiv-year [23], snap-patents [23], penn94 [23], pokec [23], genius [23], and twitch-gamers [23]. |
| Dataset Splits | Yes | We randomly split the nodes into training, validation, and test sets with a ratio of 3:1:1. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., specific GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using an 'R package, MSGLasso [21]', Adam [18], and Adam W [26] as optimizers, but does not provide specific version numbers for these software components or for broader frameworks like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | Specifically, we set learning rate = 0.01, weight decay = 0.001, dropout rate = 0.6, max epoch = 10000, and batch size = 256. We use Adam [18] as an optimizer for all models except LINKX. Adam W [26] is used with LINKX in order to comply with Lim et al. [23]. |