Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
Authors: Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, Irwin King
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on graph/image datasets show that our spectral feature augmentation outperforms baselines, and is complementary with other augmentation strategies and compatible with various contrastive losses. |
| Researcher Affiliation | Collaboration | 1The Chinese University of Hong Kong 2Australian National University 3Data61/CSIRO |
| Pseudocode | Yes | Algorithm 1: Spectral Feature Augmentation (AF ) |
| Open Source Code | No | The paper states 'Implementation and evaluation are based on Py GCL (Zhu et al. 2021a): https://github.com/Py GCL/Py GCL.' and 'Implementation/evaluation are based on Solo-learn (da Costa et al. 2022): https://github.com/vturrisi/solo-learn.', which refer to external libraries used. It does not explicitly state that the authors' specific implementation code for the proposed method is openly available, nor does it provide a direct link to their own repository. |
| Open Datasets | Yes | Datasets. We use five popular datasets (Zhu et al. 2020, 2021b; Velickovic et al. 2019), including citation networks (Cora, Cite Seer) and social networks (Wiki-CS, Amazon Computers, Amazon-Photo) (Kipf and Welling 2017; Sinha et al. 2015; Mc Auley et al. 2015; Mernyei and Cangea 2020). For graph classification, we use NCI1, PROTEIN and DD (Dobson and Doig 2003; Riesen and Bunke 2008). For image classification we use CIFAR10/100 and Image Net-100 (Deng et al. 2009). |
| Dataset Splits | Yes | Graph Datasets are randomly divided into 10%, 10%, 80% for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only mentions general terms like 'GCN layers'. |
| Software Dependencies | No | The paper mentions using 'Py GCL' and 'Solo-learn' for implementation and evaluation, but does not provide specific version numbers for these libraries or any other software dependencies (e.g., Python, PyTorch, CUDA). |
| Experiment Setup | Yes | We use Xavier initialization for the GNN parameters and train the model with Adam optimizer. For node/graph classification, we use 2 GCN layers. The logistic regression classifier is trained with 5, 000 (guaranteed converge). We also use early stopping with a patience of 20 to avoid overfitting. We set the size of the hidden dimension of nodes to from 128 to 512. For the chosen hyper-parameters see Section D.2. |