CKGConv: General Graph Convolution with Continuous Kernels
Authors: Liheng Ma, Soumyasundar Pal, Yitian Zhang, Jiaming Zhou, Yingxue Zhang, Mark Coates
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that CKGConv-based Networks outperform existing graph convolutional networks and perform comparably to the best graph transformers across a variety of graph datasets. |
| Researcher Affiliation | Collaboration | 1Department of ECE, Mc Gill University, Montreal, Canada 2Mila Quebec AI Institute, Montreal, Canada 3ILLS International Laboratory on Learning Systems, Montreal, Canada 4Huawei Noah s Ark Lab, Montreal, Canada |
| Pseudocode | No | The paper includes mathematical equations and architectural diagrams, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | The code and models are publicly available at https: //github.com/networkslab/CKGConv. |
| Open Datasets | Yes | We evaluate our proposed method on five datasets from Benchmarking GNNs (Dwivedi et al., 2022a) and another two datasets from Long-Range Graph Benchmark (Dwivedi etwed et al., 2022c). These benchmarks include diverse nodeand graph-level learning tasks such as node classification, graph classification, and graph regression. The statistics of these datasets and further details of the experimental setup are deferred to Appendix C." and "Table 6. Overview of the graph learning datasets involved in this work (Dwivedi et al., 2022a;c; Irwin et al., 2012). |
| Dataset Splits | Yes | We conduct the experiments on the standard train/validation/test splits of the evaluated benchmarks, following previous works (Ramp aˇsek et al., 2022; Ma et al., 2023). |
| Hardware Specification | Yes | The timing is conducted on a single NVIDIA V100 GPU (Cuda 11.8) and 20 threads of Intel(R) Xeon(R) Gold 6140 CPU @ 2.30GHz. |
| Software Dependencies | No | The paper mentions "Cuda 11.8" but does not provide specific version numbers for other key software components like Python or deep learning frameworks (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | The final hyperparameters are presented in Table 7 and Table 8. |