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..
Halting in Random Walk Kernels
Authors: Mahito Sugiyama, Karsten Borgwardt
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically show that halting may occur in geometric random walk kernels. We also empirically quantify its impact in simulated datasets and popular graph classification benchmark datasets. |
| Researcher Affiliation | Academia | Mahito Sugiyama ISIR, Osaka University, Japan JST, PRESTO EMAIL Karsten M. Borgwardt D-BSSE, ETH Z urich Basel, Switzerland EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and all datasets are available at: http://www.bsse.ethz.ch/mlcb/research/machine-learning/graph-kernels.html |
| Open Datasets | Yes | We collected five real-world graph classification benchmark datasets: ENZYMES, NCI1, NCI109, MUTAG, and D&D, which are popular in the graph-classification literature [13, 14]. The code and all datasets are available at: http://www.bsse.ethz.ch/mlcb/research/machine-learning/graph-kernels.html |
| Dataset Splits | Yes | The classification accuracy of each graph kernel was examined by 10-fold cross validation with multiclass C-support vector classification (libsvm2 was used), in which the parameter C for CSVC and a parameter (if one exists) of each kernel were chosen by internal 10-fold cross validation (CV) on only the training dataset. |
| Hardware Specification | Yes | We used Amazon Linux AMI release 2015.03 and ran all experiments on a single core of 2.5 GHz Intel Xeon CPU E5-2670 and 244 GB of memory. |
| Software Dependencies | Yes | All kernels were implemented in C++ with Eigen library and compiled with gcc 4.8.2. libsvm2 was used. |
| Experiment Setup | Yes | The list of parameters optimized by the internal CV is as follows: C {2 7, 2 5, . . . , 25, 27} for C-SVC, the width σ {10 2, . . . , 102} in the RBF kernel KVEH,G, the number of steps k {1, . . . , 10} in Kk , the number of iterations h {1, . . . , 10} in KWL, and λ {10 5, . . . , 10 2, λmax} in KH and KGR, where λmax = (max G,G G ) 1. |