Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
Authors: Simon S. Du, Kangcheng Hou, Russ R. Salakhutdinov, Barnabas Poczos, Ruosong Wang, Keyulu Xu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we test GNTKs on graph classification datasets and show they achieve strong performance. |
| Researcher Affiliation | Academia | Simon S. Du Institute for Advanced Study ssdu@ias.edu Kangcheng Hou Zhejiang University kangchenghou@gmail.com Barnabás Póczos Carnegie Mellon University bapoczos@cs.cmu.edu Ruslan Salakhutdinov Carnegie Mellon University rsalakhu@cs.cmu.edu Ruosong Wang Carnegie Mellon University. ruosongw@andrew.cmu.edu Keyulu Xu Massachusetts Institute of Technology keyulu@mit.edu |
| Pseudocode | No | The paper provides mathematical formulas and derivations for GNTK calculations, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Datasets. The benchmark datasets include four bioinformatics datasets MUTAG, PTC, NCI1, PROTEINS and three social network datasets COLLAB, IMDB-BINARY, IMDB-MULTI. |
| Dataset Splits | Yes | Following common practices of evaluating performance of graph classification models Yanardag and Vishwanathan [2015], we perform 10-fold cross validation and report the mean and standard deviation of validation accuracies. |
| Hardware Specification | Yes | On IMDB-B dataset, running GIN with the default setup (official implementation of Xu et al. [2019a]) takes 19 minutes on a TITAN X GPU and running GNTK only takes 2 minutes. |
| Software Dependencies | No | The paper mentions 'official implementation of Xu et al. [2019a]' but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Following common practices of evaluating performance of graph classification models Yanardag and Vishwanathan [2015], we perform 10-fold cross validation and report the mean and standard deviation of validation accuracies. More details about the experiment setup can be found in Section B of the supplementary material. For IMDBBINARY, we vary the number of BLOCK operations in {2, 3, 4, 5, 6}. For NCI1, we vary the number of BLOCK operations in {8, 10, 12, 14, 16}. For both datasets, we vary the number of MLP layers in {1, 2, 3}. |