Wasserstein Weisfeiler-Lehman Graph Kernels
Authors: Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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. |