Simple and Deep Graph Convolutional Networks
Authors: Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of GCNII against the state-of-the-art graph neural network models on a wide variety of open graph datasets. [...] We use three standard citation network datasets Cora, Citeseer, and Pubmed (Sen et al., 2008) for semi-supervised node classification. [...] Table 2 reports the mean classification accuracy (%) results on Cora, Citeseer, and Pubmed. |
| Researcher Affiliation | Collaboration | 1School of Information, Renmin University of China 2Gaoling School of Articial Intelligence, Renmin University of China [...] 5School of Data Science, Fudan University 6Alibaba Group. |
| Pseudocode | No | The paper does not include any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm', nor are there any clearly formatted pseudocode blocks. |
| Open Source Code | No | The paper does not include an explicit statement from the authors about releasing their source code for the GCNII methodology, nor does it provide a direct link to such a repository. |
| Open Datasets | Yes | We use three standard citation network datasets Cora, Citeseer, and Pubmed (Sen et al., 2008) for semi-supervised node classification. [...] For full-supervised node classification, we also include Chameleon (Rozemberczki et al., 2019), Cornell, Texas, and Wisconsin (Pei et al., 2020). [...] For inductive learning, we use Protein-Protein Interaction (PPI) networks (Hamilton et al., 2017). |
| Dataset Splits | Yes | For the semi-supervised node classification task, we apply the standard fixed training/validation/testing split (Yang et al., 2016) on three datasets Cora, Citeseer, and Pubmed, with 20 nodes per class for training, 500 nodes for validation and 1,000 nodes for testing. [...] we randomly split nodes of each class into 60%, 20%, and 20% for training, validation and testing |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper does not specify the version numbers for any key software components or libraries (e.g., Python, PyTorch, TensorFlow) used in the implementation or for running the experiments. |
| Experiment Setup | Yes | We use the Adam SGD optimizer (Kingma & Ba, 2015) with a learning rate of 0.01 and early stopping with a patience of 100 epochs to train GCNII and GCNII*. We set αℓ= 0.1 and L2 regularization to 0.0005 for the dense layer on all datasets. [...] We fix the learning rate to 0.01, dropout rate to 0.5 and the number of hidden units to 64 on all datasets. |