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About Graph Contrastive Learning With Augmentations
This document proposes a framework called Graph Contrastive Learning (GraphCL) for self-supervised pre-training of graph neural networks. GraphCL uses four types of graph augmentations and a contrastive learning approach to learn representations that are invariant to these augmentations. The authors evaluate GraphCL on several datasets and tasks, finding it achieves state-of-the-art performance for semi-supervised learning, unsupervised representation learning, transfer learning, and robustness to adversarial attacks.
Detailed Information
| Author: | ['IndritNallbani'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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