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 [1].
Probabilistically Rewired Message-Passing Neural Networks
Authors: Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, Christopher Morris
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that our approach effectively mitigates issues like over-squashing and under-reaching. In addition, on established real-world datasets, our method exhibits competitive or superior predictive performance compared to traditional MPNN models and recent graph transformer architectures. |
| Researcher Affiliation | Collaboration | Chendi Qian* Computer Science Department RWTH Aachen University, Germany EMAIL Andrei Manolache* Computer Science Department University of Stuttgart, Germany Bitdefender, Romania EMAIL Kareem Ahmed, Zhe Zeng & Guy Van den Broeck Computer Science Department University of California, Los Angeles, USA Mathias Niepert Computer Science Department University of Stuttgart, Germany Christopher Morris Computer Science Department RWTH Aachen University, Germany |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | An anonymized repository of our code can be accessed at https://anonymous.4open.science/r/PR-MPNN. Our code can be accessed at https://github.com/chendiqian/PR-MPNN/. |
| Open Datasets | Yes | Datasets To answer Q1, we utilized the TREES-NEIGHBORSMATCH dataset (Alon & Yahav, 2021). Additionally, we created the TREES-LEAFCOUNT dataset... To tackle Q2, we performed experiments with the EXP (Abboud et al., 2020) and CSL datasets (Murphy et al., 2019)... To answer Q3 (a), we used the established molecular graph-level regression datasets ALCHEMY (Chen et al., 2019), ZINC (Jin et al., 2017; Dwivedi et al., 2020), OGBG-MOLHIV (Hu et al., 2020a), QM9 (Hamilton et al., 2017), LRGB (Dwivedi et al., 2022b) and five datasets from the TUDATASET repository (Morris et al., 2020). To answer Q3 (b), we used the CORNELL, WISCONSIN, TEXAS node-level classification datasets (Pei et al., 2020). |
| Dataset Splits | Yes | For the TUDATASET, we compare with the reported scores from Giusti et al. (2023b) and use the same evaluation strategy as in Xu et al. (2019); Giusti et al. (2023b), i.e., running 10-fold cross-validation and reporting the maximum average validation accuracy. We evaluate test predictive performance based on validation performance. |
| Hardware Specification | Yes | Experiments performed on a machine with a single Nvidia RTX A5000 GPU and a Intel i9-11900K CPU. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | Table 8 lists our hyperparameters choices. For all our experiments, we use early stopping with an initial learning rate of 0.001 that we decay by half on a plateau. |