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..
Wasserstein Weisfeiler-Lehman Graph Kernels
Authors: Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we analyse how the performance of WWL compares with state-of-the-art graph kernels. In particular, we empirically observe that WWL (1) is competitive with the best graph kernel for categorically labelled data, and (2) outperforms all the state-of-the-art graph kernels for attributed graphs. |
| Researcher Affiliation | Academia | 1DEPARTMENT OF BIOSYSTEMS SCIENCE AND ENGINEERING, ETH ZURICH, SWITZERLAND 2SIB SWISS INSTITUTE OF BIOINFORMATICS, SWITZERLAND |
| Pseudocode | Yes | Figure 1 illustrates the first two steps, and Algorithm 1 summarises the whole procedure. |
| Open Source Code | Yes | We leverage these resources and make our code publicly available1. 1https://github.com/Borgwardt Lab/WWL |
| Open Datasets | Yes | All the data sets have been downloaded from Kersting et al. [20]. |
| Dataset Splits | Yes | As a classifier, we use an SVM (or a KSVM in the case of WWL) and 10-fold cross-validation, selecting the parameters on the training set only. We repeat each cross-validation split 10 times and report the average accuracy. |
| Hardware Specification | Yes | All our analyses were performed on a shared server running Ubuntu 14.04.5 LTS, with 4 CPUs (Intel Xeon E7-4860 v2 @ 2.60GHz) each with 12 cores and 24 threads, and 512 GB of RAM. |
| Software Dependencies | No | The paper mentions 'Available Python implementations can be used to compute the WL kernel [41] and the Wasserstein distance [13]' but does not provide specific version numbers for Python or these libraries. It lists Ubuntu 14.04.5 LTS as the operating system, but this is not an ancillary software component with a version number for replication. |
| Experiment Setup | Yes | As a classifier, we use an SVM (or a KSVM in the case of WWL) and 10-fold cross-validation, selecting the parameters on the training set only. We repeat each cross-validation split 10 times and report the average accuracy. We employ the same split for each evaluated method, thereby guaranteeing a fully comparable setup among all evaluated methods. Please refer to Appendix A.6 for details on the hyperparameter selection. |