On Valid Optimal Assignment Kernels and Applications to Graph Classification
Authors: Nils M. Kriege, Pierre-Louis Giscard, Richard Wilson
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed classification experiments using the C-SVM implementation LIBSVM [7]. We report mean prediction accuracies and standard deviations obtained by 10-fold cross-validation repeated 10 times with random fold assignment. |
| Researcher Affiliation | Academia | Nils M. Kriege Department of Computer Science TU Dortmund, Germany nils.kriege@tu-dortmund.de; Pierre-Louis Giscard Department of Computer Science University of York, UK pierre-louis.giscard@york.ac.uk; Richard C. Wilson Department of Computer Science University of York, UK richard.wilson@york.ac.uk |
| Pseudocode | No | The paper describes procedures and definitions mathematically and descriptively but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states "All kernels were implemented in Java" but does not provide a link or explicit statement about the release of their implementation code for the described methodology. A footnote mentions a URL for datasets, not source code. |
| Open Datasets | Yes | We tested on widely-used graph classification benchmarks from different domains [cf. 4, 23, 19, 24]: MUTAG, PTC-MR, NCI1 and NCI109 are graphs derived from small molecules, PROTEINS, D&D and ENZYMES represent macromolecules, and COLLAB and REDDIT are derived from social networks.1 The data sets, further references and statistics are available from http://graphkernels.cs. tu-dortmund.de. |
| Dataset Splits | Yes | We report mean prediction accuracies and standard deviations obtained by 10-fold cross-validation repeated 10 times with random fold assignment. Within each fold all necessary parameters were selected by crossvalidation based on the training set. |
| Hardware Specification | Yes | experiments were conducted using Oracle Java v1.8.0 on an Intel Core i7-3770 CPU at 3.4GHz (Turbo Boost disabled) with 16GB of RAM using a single processor only. |
| Software Dependencies | No | The paper mentions "C-SVM implementation LIBSVM [7]" without a version number for LIBSVM. It also mentions "Oracle Java v1.8.0" but this is a language runtime and not sufficient to meet the requirement for specific versioned libraries or solvers for reproducibility. |
| Experiment Setup | No | The paper states that "all necessary parameters were selected by crossvalidation based on the training set. This includes the regularization parameter C, kernel parameters where applicable and whether to normalize the kernel matrix," but it does not provide specific values or ranges for these parameters, which are crucial for reproducibility. |