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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Matching Node Embeddings for Graph Similarity
Authors: Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,331 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,520 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed methods on several benchmark datasets for graph classification and compare their performance to state-of-the-art graph kernels. In most cases, the proposed algorithms outperform the competing methods, while their time complexity remains very attractive. ... Section 5 evaluates the proposed algorithms and compares them with existing methods. |
| Researcher Affiliation | Academia | Giannis Nikolentzos Ecole Polytechnique and AUEB EMAIL Polykarpos Meladianos Ecole Polytechnique and AUEB EMAIL Michalis Vazirgiannis Ecole Polytechnique and AUEB EMAIL |
| Pseudocode | No | The paper describes the algorithms in detail but does not include any formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | Our code is available at http://www.db-net.aueb.gr/ nikolentzos/code/matchingnodes.zip |
| Open Datasets | Yes | We evaluated the performance of our methods on the following 6 bioinformatics datasets: MUTAG, ENZYMES, NCI1, NCI109, PTC-MR and D&D. |
| Dataset Splits | Yes | We employed a C-Support Vector Machine (SVM) classifier and in particular, the LIBSVM (Chang and Lin 2011) implementation and performed 10-fold cross-validation. |
| Hardware Specification | Yes | CPU runtimes for computing each kernel/similarity matrix as measured in Matlab R2013a on a 3.4GHz Intel Core i7 with 16Gb of RAM. |
| Software Dependencies | Yes | measured in Matlab R2013a ... we used their publicly available implementations. ... we employed a C-Support Vector Machine (SVM) classifier and in particular, the LIBSVM (Chang and Lin 2011) implementation |
| Experiment Setup | Yes | The parameter C of the SVM was optimized on the training set only. ... As regards the parameter h of the Weisfeiler-Lehman kernels, it was chosen by cross-validation on the training set for h {0, 1, . . . , 10} in the case of the subtree kernel, for h {0, 1, 2} in the case of the shortest path kernel, and for h {0, 1, . . . , 5} in the case of the proposed pyramid match kernel and the optimal assignment method. |