Taming graph kernels with random features
Authors: Krzysztof Marcin Choromanski
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically verify the quality of GRFs via various experiments, including downstream applications of graph kernels. |
| Researcher Affiliation | Collaboration | 1Google Deep Mind 2Columbia University. Correspondence to: Krzysztof Choromanski <kchoro@google.com>. |
| Pseudocode | Yes | Algorithm 1 Computing a signature vector for a given i. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology described, nor does it provide a repository link or mention code in supplementary materials. |
| Open Datasets | Yes | The real-world graphs were accessed from the repositories described in (Ivashkin, 2023). Ivashkin, V. Community graphs repository, 2023. URL https://github.com/vlivashkin/community-graphs. |
| Dataset Splits | No | The paper uses various graphs for experiments but does not provide specific details on dataset splits (e.g., train/validation/test percentages or counts), nor does it mention cross-validation or other data partitioning methodologies. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers required to replicate the experiments. |
| Experiment Setup | Yes | We took pterm = 0.1 since it worked well in several other tests (see: Sec. 5.2, Sec. 5.3). We fixed: σ2 = 0.2. The reported empirical relative Frobenium norm errors were obtained by averaging over s = 10 independent experiments. In all the experiments we used σ2 = 0.2, pterm ≈ 1/400 and K ≈ 0.6N. We chose the no of clusters nb clusters = 3. |