Official code for NeurIPS Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
We observe that common phenomena among existing GCL methods that are quite different from VCL methods, including 1) positive samples are not a must for GCL; 2) negative samples are not necessary for graph classification, neither for node classification when adopting specific normalization modules; 3) data augmentations have much less influence on GCL, as simple domain-agnostic augmentations (e.g., Gaussian noise) can also attain fairly good performance. By uncovering how the implicit inductive bias of GNNs works in contrastive learning, we theoretically provide insights into the above intriguing properties of GCL.
We provide implementations of GCL methods including GRACE, GCA, ProGCL, AutoGCL, BGRL, GraphCL, JOAO, ADGCL, InfoGraph, and a VCL method SimCLR. For running details, please refer to the README.md
file in the corresponding sub-directory.
ContraNorm/
├── README.md
├── GCL/
│ ├── README.md
│ ├── ADGCL/
│ │ ├── README.md
│ │ └── ...
│ ├── AutoGCL/
│ │ ├── README.md
│ │ └── ...
│ ├── BGRL/
│ │ ├── README.md
│ │ └── ...
│ ├── GCA/
│ │ ├── README.md
│ │ └── ...
│ ├── GRACE/
│ │ ├── README.md
│ │ └── ...
│ ├── GraphCL/
│ │ ├── README.md
│ │ └── ...
│ ├── InfoGraph/
│ │ ├── README.md
│ │ └── ...
│ ├── ProGCL/
│ │ ├── README.md
│ │ └── ...
│ ├── JOAO/
│ │ ├── README.md
│ │ └── ...
│ └── datasets/
├── VCL/
│ ├── README.md
│ └── ...
└── ...
If you use our code, please cite
@inproceedings{guo2023architecture,
title={Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning},
author={Xiaojun Guo and Yifei Wang and Zeming Wei and Yisen Wang},
booktitle={NeurIPS},
year={2023}
}