Weisfeiler and Lehman Go Cellular: CW Networks
Authors: Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montufar, Michael Bronstein
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we validate the theoretical and empirical properties of our proposed message passing scheme in controlled scenarios as well as in real-world graph classification problems, with a focus on large scale molecular benchmarks. Table 1: Classification accuracy on CSL. Table 2: TUDatasets. Table 3: ZINC (MAE), ZINC-FULL (MAE) and Mol-HIV (ROC-AUC). |
| Researcher Affiliation | Collaboration | Cristian Bodnar University of Cambridge cb2015@cam.ac.uk Fabrizio Frasca Imperial College London & Twitter ffrasca@twitter.com UCLA otter@math.ucla.edu Yu Guang Wang MPI-MIS, SJTU & UNSW yuguang.wang@unsw.edu.au Pietro Liò University of Cambridge pl219@cam.ac.uk Guido Montúfar MPI-MIS & UCLA montufar@math.ucla.edu Michael Bronstein Imperial College London & Twitter mbronstein@twitter.com |
| Pseudocode | No | The paper describes the steps of the CWL algorithm in a numbered list (e.g., '1. Given a regular cell complex X, all the cells σ are initialised with the same colour. 2. Given the colour ctσ of cell σ at iteration t, we compute the colour of cell σ at the next iteration ct+1σ by injectively mapping the multi-sets of colours belonging to the adjacent cells of σ using a perfect HASH function: ct+1 3. The algorithm stops when a stable colouring is reached.'), but it is presented in prose rather than a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/twitter-research/cwn. |
| Open Datasets | Yes | CSL Circular Skip Link dataset was first introduced in [57]... SR Similarly to Bodnar et al. [8] and [10]... 3Data available at: http://users.cecs.anu.edu.au/~bdm/data/graphs.html. TUD We test our model on 8 TUDataset benchmarks [56]... ZINC We study the effectiveness of cellular message passing on larger scale molecular benchmarks from the ZINC database [68]... Mol-HIV We additionally test our model on the molecular ogbg-molhiv dataset from the Open Graph Benchmark [40]... |
| Dataset Splits | Yes | We follow the same evaluation setting as Dwivedi et al. [24]: 5-fold cross validation procedure and 20 different random weight initialisations. We follow the training and evaluation procedures in [24]. |
| Hardware Specification | No | The paper states, 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Section 5 and Appendix E.' However, the provided text of Section 5 does not contain specific hardware details, and Appendix E is not included. |
| Software Dependencies | No | The paper cites PyTorch [59] and PyTorch Geometric [28], indicating their use. However, it does not specify the exact version numbers of these or any other software libraries or dependencies required for replication. |
| Experiment Setup | Yes | For simplicity, in all experiments we employ a model which stacks CWN layers with local aggregators as in GIN [74]... See Appendix E for details on feature initialisation, message passing and readout operations, hyperparameters, implementation and benchmark statistics. The training setting and evaluation procedure follow those in Xu et al. [74]. We take the architecture in [27] as reference and replicate the same hyperparameter setting in our model, including the use of only 2 message passing layers. |