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..
Training-free Graph Neural Networks and the Power of Labels as Features
Authors: Ryoma Sato
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we confirm that TFGNNs outperform existing GNNs in the training-free setting and converge with much fewer training iterations than traditional GNNs. |
| Researcher Affiliation | Academia | Ryoma Sato EMAIL National Institute of Informatics |
| Pseudocode | No | The paper defines the TFGNN architecture and its initialization using mathematical equations (19-29) rather than a separate pseudocode or algorithm block. |
| Open Source Code | Yes | Reproducibility: Our code is available at https://github.com/joisino/laf. |
| Open Datasets | Yes | We use the Planetoid datasets (Cora, Cite Seer, Pub Med) [54], Coauthor datasets, and Amazon datasets [42] in the experiments. |
| Dataset Splits | Yes | We use 20 nodes per class for training, 500 nodes for validation, and the rest for testing in the Planetoid datasets following Kipf et al. [20], and use 20 nodes per class for training, 30 nodes per class for validation, and the rest for testing in the Coauthor and Amazon datasets following Shchur et al. [42]. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions using Adam W for training but does not provide specific version numbers for any software libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | We use three layered models with the hidden dimension 32 unless otherwise specified. We train all the models with Adam W [25] with learning rate 0.0001 and weight decay 0.01. |