AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators
Authors: Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, Xiang Zhang8892-8900
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on semi-supervised learning, unsupervised learning, and transfer learning demonstrate the superiority of our Auto GCL framework over the state-of-the-arts in graph contrastive learning. |
| Researcher Affiliation | Collaboration | Yihang Yin1, Qingzhong Wang2, Siyu Huang3, Haoyi Xiong2, Xiang Zhang4 1Nanyang Technological University 2Baidu Research 3Harvard University 4The Pennsylvania State University yyin009@e.ntu.edu.sg, wangqingzhong@baidu.com, huang@seas.harvard.edu, xionghaoyi@baidu.com, xzz89@psu.edu |
| Pseudocode | Yes | Algorithm 1: Naive training strategy (naive-strategy).", "Algorithm 2: Joint training strategy (joint-strategy). |
| Open Source Code | Yes | Our code is available at https://github.com/Somedaywilldo/Auto GCL. |
| Open Datasets | Yes | We perform 100 epochs of supervised pre-training on the pre-processed Ch EMBL dataset ((Mayr et al. 2018; Gaulton et al. 2012)), which contains 456K molecules with 1,310 kinds of diverse and extensive biochemical assays." and "We perform semi-supervised graph classification task on TUDataset (Morris et al. 2020)." and "we trained a view generator on MNIST-Superpixel dataset (Monti et al. 2017)". |
| Dataset Splits | Yes | We perform a 10-fold cross validation on every dataset. For each fold, we employ 90% of the total data as the unlabeled data for contrastive pre-training, and 10% as the labeled testing data." and "We employ a 10-fold cross validation on each dataset. For each fold, we use 80% of the total data as the unlabeled data, 10% as labeled training data, and 10% as labeled testing data. |
| Hardware Specification | No | The paper does not specify the exact hardware used for experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow, etc.) that would be required for replication. |
| Experiment Setup | Yes | We train the GIN with a batch size of 128 and a learning rate of 0.001. There are 30 epochs of contrastive pre-training under the naive-strategy." and "We use a hidden size of 300 for the classifier, a hidden size of 128 for the view generator. We train the model using a batch size of 256 and a learning rate of 0.001." and "All augmentation ratios are set to 0.2, which is also the default setting in Graph CL. |