The field of anomaly detection and graph learning is moving towards more efficient and scalable solutions. Researchers are exploring new approaches to improve the accuracy and robustness of anomaly detection methods, including the use of fog intelligence, clean-view perspectives, and unconditional graph diffusion models. These innovative methods aim to address the limitations of traditional approaches, such as the presence of interfering edges and the need for explicit noise conditioning. Notably, the development of distributed machine learning architectures and proactive anomaly detection-based scheduling frameworks is enabling more effective management of large-scale distributed wireless networks and crowdsourced cloud-edge platforms. Noteworthy papers include: The paper 'Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective' which proposes a Clean-View Enhanced Graph Anomaly Detection framework to address the limitation of interfering edges. The paper 'Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models' which challenges the assumption that explicit noise-level conditioning is essential for Graph Diffusion Models.