Weisfeiler-Leman at the margin: When more expressivity matters
Authors: Billy Joe Franks, Christopher Morris, Ameya Velingker, Floris Geerts
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study confirms the validity of our theoretical findings. and 5. Experimental evaluation and Results and discussion In the following, we answer questions Q1 to Q4. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Kaiserslautern-Landau 2Department of Computer Science, RWTH Aachen University 3Google Research 4Department of Computer Science, University of Antwerp. |
| Pseudocode | No | The paper describes algorithms in prose and mathematical notation but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The source code of all methods and evaluation procedures is available at https://www.github.com/chrsmrrs/wl_vc_expressivity. |
| Open Datasets | Yes | We used the well-known graph classification benchmark datasets from Morris et al. (2020a); see Table 2 for dataset statistics and properties.2All datasets are publicly available at www.graphlearning.io. |
| Dataset Splits | Yes | In both cases, we used 10-fold cross-validation. We repeated each 10-fold cross-validation ten times with different random folds and report average training and testing accuracies and standard deviations. and using a validation set sampled uniformly at random from the training fold (using 10 % of the training fold). |
| Hardware Specification | Yes | All kernel experiments were conducted on a workstation with 512 GB of RAM using a single CPU core. and All MPNN experiments were conducted on a workstation with 512 GB of RAM using a single core and one NVIDIA Tesla A100s with 80 GB of GPU memory. |
| Software Dependencies | No | We implemented the (normalized) 1-WL, 1-WLOA, 1-WLF, and the 1-WLOAF in Python. and computed the classification accuracies using the C-SVM implementation of LIBSVM (Chang & Lin, 2011). and computed the classification accuracies using the linear SVM implementation of LIBLINEAR (Fan et al., 2008). |
| Experiment Setup | Yes | For the experiments on the TUDATSETS, following the evaluation method proposed in Morris et al. (2020a), the C-parameter and numbers of iterations were selected from {10 3, 10 2, . . . , 102, 103} and {1, . . . , 5}, respectively, using a validation set sampled uniformly at random from the training fold (using 10 % of the training fold). and We used an initial learning rate of 0.01 across all experiments with an exponential learning rate decay with patience of 5, a batch size of 128, and set the maximum number of epochs to 200. and We used mean pooling and a two-layer MLP using a dropout of 0.5 after the first layer for all experiments for the final classification. |