Halting in Random Walk Kernels
Authors: Mahito Sugiyama, Karsten Borgwardt
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 mahito@ar.sanken.osaka-u.ac.jp Karsten M. Borgwardt D-BSSE, ETH Z urich Basel, Switzerland karsten.borgwardt@bsse.ethz.ch |
| 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. |