Covered Forest: Fine-grained generalization analysis of graph neural networks
Authors: Antonis Vasileiou, Ben Finkelshtein, Floris Geerts, Ron Levie, Christopher Morris
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study supports our theoretical insights, improving our understanding of MPNNs generalization properties. and 6. Experimental study |
| Researcher Affiliation | Academia | 1RWTH Aachen University, Germany 2University of Oxford, UK 3University of Antwerp, Belgium 4Technion Israel Institute of Technology, Israel. |
| Pseudocode | No | The paper describes algorithms such as the 1-dimensional Weisfeiler Leman algorithm and its variants, but these descriptions are presented in prose and mathematical notation rather than dedicated pseudocode or algorithm blocks. |
| Open Source Code | Yes | See https://github.com/benfinkelshtein/ Covered Forests for source code and instructions to reproduce all results. |
| Open Datasets | Yes | Additionally, we experimented with the binary classification real-world datasets MUTAG, NCI1, MCF-7H (Morris et al., 2020a), and OGBG-MOLHIV (Hu et al., 2020) |
| Dataset Splits | Yes | We used a random 80/10/10 split for training/validation/testing. |
| Hardware Specification | Yes | All models were implemented with Py Torch Geometric (Fey & Lenssen, 2019) and executed on a system with 128GB of RAM and an Nvidia Tesla A100 GPU with 48GB of memory. |
| Software Dependencies | No | All models were implemented with Py Torch Geometric (Fey & Lenssen, 2019) and executed on a system with 128GB of RAM and an Nvidia Tesla A100 GPU with 48GB of memory. - While software is mentioned, specific version numbers for key libraries like PyTorch or CUDA are not provided. and Adam optimizer (Kingma & Ba, 2015) |
| Experiment Setup | Yes | We tuned the feature dimension across the set 32, 64, 128, 256 based on validation set performance, training MUTAG and NCI1 for 100 epochs and MCF-7H and OGBG-MOLHIV for 20 epochs using the Adam optimizer (Kingma & Ba, 2015). The training setup included a learning rate of 0.001, a batch size of 128, and no learning rate decay or dropout across all datasets. |