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 [1].
Matching Node Embeddings for Graph Similarity
Authors: Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis
AAAI 2017 | Venue PDF | LLM Run Details
| 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. |