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 Kernels: A Survey
Authors: Giannis Nikolentzos, Giannis Siglidis, Michalis Vazirgiannis
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. ... In Section 7, we experimentally evaluate the performance of many graph kernels on several widely-used graph classification benchmark datasets. |
| Researcher Affiliation | Academia | Giannis Nikolentzos EMAIL LIX, Ecole Polytechnique Palaiseau, 91120, France; Ioannis Siglidis EMAIL LIGM, Ecole des Ponts, Universit e Gustave Eiffel, CNRS Marne-la-Vall ee, 77420, France; Michalis Vazirgiannis EMAIL LIX, Ecole Polytechnique Palaiseau, 91120, France. All listed institutions are academic or public research organizations in France, and the email domains also indicate academic affiliations. |
| Pseudocode | No | The paper describes algorithms and procedures (e.g., Geometric Random Walk Kernel, Weisfeiler-Lehman Framework, Neighborhood Hash Kernel) in descriptive text, but it does not include formally structured pseudocode blocks or algorithms with numbered steps or code-like formatting. |
| Open Source Code | No | Specifically, we made use of the Gra Ke L library which contains implementations of a large number of graph kernels (Siglidis et al., 2020). The authors state that they *used* an existing open-source library (GraKeL) for their experimental comparison, rather than releasing their own source code specifically for the methodologies described within this survey paper. |
| Open Datasets | Yes | All datasets are publicly available (Kersting et al., 2016). ... Kersting, K., Kriege, N. M., Morris, C., Mutzel, P., & Neumann, M. (2016). Benchmark data sets for graph kernels.. http://graphkernels.cs.tu-dortmund.de. |
| Dataset Splits | Yes | Therefore, we perform 10-fold cross-validation to obtain an estimate of the generalization performance of each method. For the common datasets, we use the splits (and results) provided by Errica et al. (2020). |
| Hardware Specification | Yes | All experiments were performed on a cluster of 80 Intel Xeon CPU E7 4860 @ 2.27GHz with 1TB RAM. |
| Software Dependencies | No | Specifically, we made use of the Gra Ke L library which contains implementations of a large number of graph kernels (Siglidis et al., 2020). We also employed a Support Vector Machine (SVM) classifier and in particular, the LIB-SVM implementation (Chang & Lin, 2011). While specific libraries (GraKeL, LIB-SVM) are mentioned, their version numbers are not explicitly provided. |
| Experiment Setup | Yes | Within each fold, the parameter C of the SVM and the hyperparameters of the kernels (see below) and GNNs were chosen based on a validation experiment on a single 90% 10% split of the training data. We chose the value of parameter C from {10 7, 10 5, . . . , 105, 107}. Moreover, we normalized all kernel values as follows ˆk(Gi, Gj) = k(Gi,Gj)/√k(Gi,Gi) k(Gj,Gj) for any graphs Gi, Gj. ... The values of the different hyperparameters of the kernels are shown in Table 4. |