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..
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
Authors: Lecheng Kong, Jiarui Feng, Hao Liu, Dacheng Tao, Yixin Chen, Muhan Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on many datasets, showing that MAG-GNN achieves competitive performance to state-of-the-art methods and even outperforms many subgraph GNNs. |
| Researcher Affiliation | Collaboration | Lecheng Kong1 Jiarui Feng1 Hao Liu1 Dacheng Tao2 Yixin Chen1 Muhan Zhang3 EMAIL, EMAIL, EMAIL 1Washington University in St. Louis 2JD Explore Academy 3Peking University |
| Pseudocode | Yes | Algorithm 1 RL-Experience Algorithm 2 ORD-Train Algorithm 3 SIMUL-Train Algorithm 4 PRE-Train |
| Open Source Code | Yes | 1The code can be found at https://github.com/Lecheng Kong/MAG-GNN |
| Open Datasets | Yes | We use the QM9 dataset provided by Pytorch-Geometric [9], and we use a train/valid/test split ratio of 0.8/0.1/0.1. ... We use the ZINC dataset provided by Pytorch-Geometric [9] and use the official split. We take OGBG-MOLHIV dataset from the Open Graph Benchmark package [14] and use their official split. |
| Dataset Splits | Yes | We use the QM9 dataset provided by Pytorch-Geometric [9], and we use a train/valid/test split ratio of 0.8/0.1/0.1. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU/GPU models, memory) used for running its experiments. It mentions software implementations but not hardware. |
| Software Dependencies | No | The paper states 'All models are implemented in DGL [29] and Py Torch [25].' However, it does not provide specific version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We summarize the hyperparameters used for different datasets in Table 8 and 9. |