Graph Filtration Kernels
Authors: Till Schulz, Pascal Welke, Stefan Wrobel8196-8203
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the expressive power of our graph kernels and show significant improvements over state-of-the-art graph kernels in terms of predictive performance on various real-world benchmark datasets. We empirically validate our theoretical findings on the expressive power of our kernels and furthermore provide experiments on real-world benchmark datasets which show a favorable performance of our approach compared to state-of-the-art graph kernels. |
| Researcher Affiliation | Academia | Till Schulz1, Pascal Welke1, Stefan Wrobel1,2,3 1 Universit at Bonn, Germany 2 Fraunhofer IAIS, Sankt Augustin, Germany 3 Fraunhofer Center for Machine Learning, Sankt Augustin, Germany {schulzth, welke, wrobel}@cs.uni-bonn.de |
| Pseudocode | No | The paper describes algorithms and methods in prose and mathematical notation but does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Available at https://github.com/mlai-bonn/wl-filtration-kernel |
| Open Datasets | Yes | The experiments are conducted on the well-established molecular datasets DHFR, NCI1 and PTC-MR (obtained from Morris et al. 2020) as well as the large network benchmark datasets IMDB-BINARY (obtained from Morris et al. 2020) and EGO-1 to EGO-4. |
| Dataset Splits | Yes | We measure the accuracies obtained by support vector machines (SVM) using a 10-fold stratified crossvalidation. A grid search over sets of kernel specific parameters is used for optimal training. We perform 10 such crossvalidations and report the mean and standard deviation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | No | The paper mentions a "grid search over sets of kernel specific parameters" for optimal training but does not provide the specific hyperparameter values or ranges used in the main text. |