A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"
Authors: Asiri Wijesinghe, Qing Wang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically verify the strength of our model on different graph learning tasks. It is shown that our model consistently improves the state-of-the-art methods on the benchmark tasks without sacrificing computational simplicity and efficiency. |
| Researcher Affiliation | Academia | Asiri Wijesinghe & Qing Wang School of Computing, Australian National University, Canberra, Australia {asiri.wijesinghe, qing.wang}@anu.edu.au |
| Pseudocode | No | The paper describes mathematical formulations and equations for its model but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation can be found at: https://github.com/wokas36/Graph SNN |
| Open Datasets | Yes | We use five datasets: three citation network datasets Cora, Citeseer, and Pubmed (Sen et al., 2008) for semi-supervised document classification, one knowledge graph dataset NELL (Carlson et al., 2010) for semi-supervised entity classification, and one OGB dataset ogbn-arxiv from (Hu et al., 2020). |
| Dataset Splits | Yes | (1) the standard splits in Kipf & Welling (2017), i.e., 20 nodes from each class for training, 500 nodes for validation and 1000 nodes for testing, for which the results are presented in Table 1; (2) the random splits in Pei et al. (2020), i.e., randomly splitting nodes into 60%, 20% and 20% for training, validation and testing, respectively, for which the results are presented in Table 13 in Appendix B. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like "Adam optimizer" and "PyTorch" in the context of experimental setup, but it does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Experimental setup. We use the Adam optimizer (Kingma & Ba, 2015) and λ = 1. For ogbn-arxiv, our models are trained for 500 epochs with the learning rate 0.01, dropout 0.5, hidden units 256, and γ = 0.1. For the other datasets, we use 200 epochs with the learning rate 0.001, and choose the best values for weight decay from {0.001, 0.002, ..., 0.009} and hidden units from {64, 128, 256, 512}. For γ and dropout at each layer, the best value for each model in each dataset is selected from {0.1, 0.2, ..., 0.6}. Graph SNNGAT uses the attention dropout 0.6 and 8 multi-attention heads. Graph SNNGraph SAGE uses the neighborhood sample size 25 with the mean aggregation. |