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..
Graph Filtration Kernels
Authors: Till Schulz, Pascal Welke, Stefan Wrobel8196-8203
AAAI 2022 | Venue PDF | 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 EMAIL |
| 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. |