Self-Attention Graph Pooling
Authors: Junhyun Lee, Inyeop Lee, Jaewoo Kang
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that our method achieves superior graph classification performance on the benchmark datasets using a reasonable number of parameters. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, Korea University, Seoul, Korea. |
| Pseudocode | No | The paper contains mathematical equations and figures illustrating the model architecture, but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available on Github 1 https://github.com/inyeoplee77/SAGPool |
| Open Datasets | Yes | Five datasets with a large number of graphs (> 1k) were selected among the benchmark datasets (Kersting et al., 2016). The statistics of the datasets are summarized in Table 1. |
| Dataset Splits | Yes | In our experiments, we evaluated the pooling methods over 20 random seeds using 10-fold cross validation. A total of 200 testing results were used to obtain the final accuracy of each method on each dataset. 10 percent of the training data was used for validation in the training session. |
| Hardware Specification | Yes | Our experiments were run on a NVIDIA Titan Xp GPU. |
| Software Dependencies | No | We implemented all the baselines and SAGPool using Py Torch (Paszke et al., 2017) and the geometric deep learning extension library provided by Fey & Lenssen. While software names are mentioned, specific version numbers are not provided. |
| Experiment Setup | Yes | We used the Adam optimizer (Kingma & Ba, 2014), early stopping criterion, patience, and hyperparameter selection strategy for the global pooling architecture and hierarchical pooling architecture. We stopped the training if the validation loss did not improve for 50 epochs in an epoch termination condition with a maximum of 100k epochs... The optimal hyperparameters are obtained by grid search. The ranges of grid search are summarized in Table 2. |