Randomized Schur Complement Views for Graph Contrastive Learning
Authors: Vignesh Kothapalli
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on node and graph classification benchmarks demonstrate that our technique consistently outperforms predefined and adaptive augmentation approaches to achieve state-of-the-art results. |
| Researcher Affiliation | Academia | 1Courant Institute of Mathematical Sciences, New York University, New York, USA. |
| Pseudocode | Yes | The paper contains structured pseudocode blocks: "Algorithm 1 r Lap" and "Algorithm 2 A generalized GCL framework with r Lap." |
| Open Source Code | Yes | The code is available at: https://github.com/kvignesh1420/rlap |
| Open Datasets | Yes | We experiment on 10 widely used node and graph classification datasets. For node classification, we experiment on CORA (Mc Callum et al., 2000), PUBMED (Sen et al., 2008), AMAZON-PHOTO, COAUTHOR-CS and COAUTHOR-PHY (Shchur et al., 2018). For graph classification, we use the MUTAG (Debnath et al., 1991), PROTEINS-full (PROTEINS) (Borgwardt et al., 2005), IMDB-BINARY, IMDB-MULTI (Yanardag & Vishwanathan, 2015) and NCI1 (Wale et al., 2008) datasets. All the datasets are available in Py G and can be imported directly. |
| Dataset Splits | Yes | For testing, we follow a linear evaluation protocol (Velickovic et al., 2019; Zhu et al., 2021b) where the embeddings learned by the encoders in unsupervised settings are classified using a logistic regression classifier with a train, validation and test split of 10%, 10%, 80% respectively. |
| Hardware Specification | Yes | We conduct experiments on a virtual machine with 8 Intel(R) Xeon(R) Platinum 8268 CPUs, 24GB of RAM and 1 Quadro RTX 8000 GPU with 32GB of allocated memory. |
| Software Dependencies | Yes | For reproducible experiments, we leverage the open-source Py GCL framework by Zhu et al. (2021b) along with Py Torch 1.12.1 (Paszke et al., 2019) and Py Torch-Geometric (Py G) 2.1.0 (Fey & Lenssen, 2019). |
| Experiment Setup | Yes | The perturbation ratios γ1, γ2 are selected from {0.0, 0.1, 0.2, 0.3, 0.4, 0.5} to prevent excessive graph corruption. The number of layers in the encoder are chosen from {2, 4, 8}, hidden feature dimensions are chosen from {128, 256, 512}, learning rate is chosen from {10-2, 10-3, 10-4}, weight decay is set to 10-5 and the maximum epoch count is set to 2000. An early stopping strategy with a patience interval of 50 epochs with respect to contrastive loss is also employed. Finally, we choose the Adam optimizer for learning the embeddings. The selected hyper-parameters based on a grid search are reported in Table 12, 13, 14, 15 and can be reproduced with the provided code and instructions. |