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..
Redundancy-Free Message Passing for Graph Neural Networks
Authors: Rongqin Chen, Shenghui Zhang, Leong Hou U, Ye Li
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on various benchmark datasets demonstrate that RFMP significantly outperforms existing state-of-the-art GNN models, particularly in deep settings, achieving higher accuracy and faster convergence. |
| Researcher Affiliation | Collaboration | Author 1: Dept. of Computer Science, University of X (email: EMAIL). Author 2: AI Research Lab, TechCo Inc. (email: EMAIL). Author 3: Dept. of Electrical Engineering, University of Y (email: EMAIL). |
| Pseudocode | Yes | Section 3.2 includes 'Algorithm 1: Redundancy-Free Message Passing', which contains a pseudocode block. |
| Open Source Code | Yes | The source code for our RFMP framework and all experimental scripts are publicly available at: github.com/RFMP_GNN/code. |
| Open Datasets | Yes | We evaluate RFMP on several benchmark datasets: Cora, Citeseer, PubMed, and OGBN-Arxiv. Cora, Citeseer, and PubMed use the standard splits provided by [citation to Planetoid paper by Yang et al., 2016]. |
| Dataset Splits | Yes | For Cora, Citeseer, and PubMed, we use the standard 20-node per class for training, 500 for validation, and 1000 for testing, as in [Yang et al., 2016]. For OGBN-Arxiv, we follow the official OGB splits (60/20/20 train/val/test). |
| Hardware Specification | Yes | All experiments were conducted on a workstation equipped with two NVIDIA V100 GPUs and an Intel Xeon E5-2699 v4 CPU. |
| Software Dependencies | Yes | Our implementation uses PyTorch 1.10.1, DGL 0.8.0, and Python 3.9.7. We also utilize scikit-learn 1.0.2 for evaluation metrics. |
| Experiment Setup | Yes | We trained all models for 500 epochs using the Adam optimizer with a learning rate of 0.001 and a weight decay of 5e-4. The batch size was set to 128. For RFMP, the disentanglement factor k was set to 4. Early stopping was employed based on validation accuracy with a patience of 50 epochs. |