Graph Wavelet Neural Network
Authors: Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed.Experimental results demonstrate that our method consistently outperforms previous spectral CNNs on three benchmark datasets, i.e., Cora, Citeseer, and Pubmed.4 EXPERIMENTS |
| Researcher Affiliation | Academia | 1CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences; 2School of Computer and Control Engineering, University of Chinese Academy of Sciences Beijing, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | To evaluate the proposed GWNN, we apply GWNN on semi-supervised node classification, and conduct experiments on three benchmark datasets, namely, Cora, Citeseer and Pubmed (Sen et al., 2008). |
| Dataset Splits | Yes | The partition of datasets is the same as GCN (Kipf & Welling, 2017) with an additional validation set of 500 labeled samples to determine hyper-parameters.Following the experimental setup of GCN (Kipf & Welling, 2017), we fetch 20 labeled nodes per class in each dataset to train the model. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and toolboxes (GSP toolbox) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We train a two-layer graph wavelet neural network with 16 hidden units.We adopt the Adam optimizer (Kingma & Ba, 2014) for parameter optimization with an initial learning rate lr = 0.01.For Cora, s = 1.0 and t = 1e 4. For Citeseer, s = 0.7 and t = 1e 5. For Pubmed, s = 0.5 and t = 1e 7.To avoid overfitting, dropout (Srivastava et al., 2014) is applied. |