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Article summary:
1. AutoGCL is a novel framework for graph contrastive learning that uses learnable view generators to generate effective contrastive samples.
2. AutoGCL employs an auto augmentation strategy to introduce adequate augmentation variances in the contrastive learning procedure.
3. Extensive experiments demonstrate the superiority of AutoGCL over existing methods, and visualization results confirm that it can deliver more compact and semantically meaningful contrastive samples.
Article analysis:
The article is generally trustworthy and reliable, as it provides evidence for its claims through extensive experiments and visualizations. The authors also provide a detailed description of their proposed method, which makes it easier to understand how it works. However, there are some potential biases in the article that should be noted. For example, the authors only discuss the advantages of their proposed method without exploring any potential drawbacks or counterarguments. Additionally, they do not mention any possible risks associated with using their method, such as data privacy concerns or unintended consequences of using automated view generation techniques. Furthermore, the article does not present both sides equally; instead, it focuses solely on promoting the benefits of AutoGCL without considering other methods or approaches that could be used for graph contrastive learning.