1. The paper should acknowledge related works that are pertinent to the proposed learnable data augmentation, such as [a] and [b]. It is crucial to cite and discuss the distinctions between these works and the proposed approach, providing readers with a clear understanding of the novel contributions made by this study.
2. The paper predominantly explores the concept of applying learnable data augmentation for graph anomaly detection. While this is valuable, investigating its applicability in broader graph learning tasks, such as node classification with contrastive learning, could significantly expand its scope. For example, how about its benefits to generic graph contrastive learning tasks, compared to existing contrastive techniques?
3. While consistency training might usually be deployed on unlabeled data, I wonder if it would be beneficial to utilize labeled data for consistency training as well. Specifically, labeled data has exact labels, which might provide effective information for consistency training the model in dealing with the taks of graph anomaly detection.
[a] Graph Contrastive Learning Automated. Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang. ICML 2021
[b] Graph Contrastive Learning with Adaptive Augmentation. Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. WWW 2021.
3. While consistency training might usually be deployed on unlabeled data, I wonder if it would be beneficial to utilize labeled data for consistency training as well. Specifically, labeled data has exact labels, which might provide effective information for consistency training the model in dealing with the taks of graph anomaly detection. [a] Graph Contrastive Learning Automated. Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang. ICML 2021 [b] Graph Contrastive Learning with Adaptive Augmentation. Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. WWW 2021.