Simple Spectral Graph Convolution
Authors: Hao Zhu, Piotr Koniusz
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluations show that S2GC with a linear learner is competitive in text and node classification tasks. Moreover, S2GC is comparable to other state-of-the-art methods for node clustering and community prediction tasks. In this section, we evaluate the proposed method on four different tasks: node clustering, community prediction, semi-supervised node classification and text classification. |
| Researcher Affiliation | Academia | Hao Zhu, Piotr Koniusz Australian National University Canberra, Australia {hao.zhu,piotr.koniusz}@anu.edu.au Data61/CSIRO Canberra, Australia |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/allenhaozhu/SSGC. |
| Open Datasets | Yes | We compare S2GC with three variants of clustering... on four datasets: Cora, Cite Seer, Pub Med, and Wiki... For the semi-supervised node classification task, we apply the standard fixed training, validation and testing splits (Yang et al., 2016) on the Cora, Citeseer, and Pubmed datasets... We ran our experiments on five widely used benchmark corpora including the Movie Review (MR), 20-Newsgroups (20NG), Ohsumed, R52 and R8 of Reuters 21578. |
| Dataset Splits | Yes | For the semi-supervised node classification task, we apply the standard fixed training, validation and testing splits (Yang et al., 2016) on the Cora, Citeseer, and Pubmed datasets, with 20 nodes per class for training, 500 nodes for validation and 1,000 nodes for testing. ... The 10% of training set is randomly selected for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' and other software components like 'Adam SGD optimizer' and 'Meta Opt package' but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | We use the Adam SGD optimizer (Kingma & Ba, 2014) with a learning rate of 0.02 to train S2GC. We set α = 0.05 and K = 16 on all datasets. ... For Text GCN, SGC, and our approach, the embedding size of the first convolution layer is 200 and the window size is 20. We set the learning rate to 0.02, dropout rate to 0.5 and the decay rate to 0. ... we trained our method and Text GCN for a maximum of 200 epochs using the Adam (Kingma & Ba, 2014) optimizer, and we stop training if the validation loss does not decrease for 10 consecutive epochs. |