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Article summary:
1. A Traditional Chinese Medicine (TCM) Attributed Heterogeneous Information Network (TAHIN) has been established to model massive formulae.
2. A novel hybrid-scales graph contrastive learning framework has been proposed to learn high-quality node representations in an unsupervised manner.
3. Extensive experiments demonstrate the effectiveness and interpretability of the proposed method.
Article analysis:
The article is generally reliable and trustworthy, as it provides a detailed description of the proposed method and its results, as well as a link to the source code and datasets used in the experiments. The authors have also provided evidence for their claims by citing relevant research papers in the field of TCM. However, there are some potential biases that should be noted. For example, the authors do not discuss any possible risks associated with using their proposed method or any potential limitations that may arise from its use. Additionally, they do not provide any counterarguments or explore alternative methods that could be used for discovering regularities in TCM formulae. Furthermore, there is no discussion of how this method could be applied in practice or what implications it may have for clinical treatment or poly-pharmacology research.