The Multiscale Laplacian Graph Kernel
Authors: Risi Kondor, Horace Pan
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested the efficacy of the MLG kernel by performing classification on benchmark bioinformatics datasets using a binary C-SVM solver [21], and compared our classification results against those from other representative graph kernels from the literature: the Weisfeiler Lehman Kernel, the Weisfeiler Lehman Edge Kernel [9], the Shortest Path Kernel [6], the Graphlet Kernel [9], and the p-random Walk Kernel [5]. |
| Researcher Affiliation | Academia | Risi Kondor Department of Computer Science Department of Statistics University of Chicago Chicago, IL 60637 risi@cs.uchicago.edu Horace Pan Department of Computer Science University of Chicago Chicago, IL 60637 hopan@uchicago.edu |
| Pseudocode | No | The paper describes algorithms and procedures in prose and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | Yes | We tested the efficacy of the MLG kernel by performing classification on benchmark bioinformatics datasets...MUTAG[22] PTC[23] ENZYMES[2] PROTEINS[2] NCI1[24] NCI109[24] |
| Dataset Splits | Yes | On the other 80% we did 10 fold cross validation to select the parameters for each kernel method to be used on the test set and repeated this setup 10 times. |
| Hardware Specification | Yes | All experiments were done on a 16 core Intel E5-2670 @ 2.6GHz processor with 32 GB of memory. |
| Software Dependencies | No | The paper mentions using a "binary C-SVM solver [21]" (referring to LibSVM), but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the Weisfeiler Lehman kernels, the height parameter h is chosen from {1, 2, ..., 5}, the random walk size p for the p-random walk kernel was chosen from {1, 2, ..., 5}, for the Graphlets kernel the graphlet size n was chosen from {3, 4, 5}. For the parameters of the MLG kernel: we chose η from {0.01, 0.1, 1}, radius size n from {1, 2, 3}, number of levels l from {1, 2, 3}, and fixed gamma to be 0.01. |