## 4 Methodology
### Diffusion Augmentation Module  
In summary of the foregoing, the Graph Neural Network (GNN) method emerges as an excellent choice for processing center anomaly detection. However, typical GNN models often require a substantial amount of training data and a considerable labeling rate. In practical scenarios, we encountered limitations in the availability of fault data, with the quantity of unlabeled data often far exceeding that of labeled data. While theoretically, these issues could be alleviated by increasing data collection efforts, data augmentation and labeling often demand several times the manpower and resources. This is particularly true as many application domains require high levels of expertise and experience from practitioners for labeling, further constraining the feasibility of large-scale data augmentation and labeling. Therefore, rather than directly augmenting the original data, it is more prudent to explore methods to unearth latent information within existing data and effectively leverage limited data resources. In the fields of image and natural language processing, the use of generative models to address data scarcity has been proven to be an effective strategy [35; 36]. Generative models, such as diffusion models, can produce training samples that closely resemble the original dataset. Although these generated samples are artificial, they still exhibit features related to real samples, effectively enlarging the scale and improving the quality of the training dataset. Diffusion models, currently a popular class of generative models, employ a differentiable inverse process and gradually transform the target distribution into the initial distribution, exhibiting strong generative capabilities. Leveraging the concept of diffusion models, nodes and edges on a diffusion graph are diffused, extending the source data and generating original data that may not exist in all samples, thereby significantly overcoming the limitations imposed by sample size. In processing center anomaly detection, the data environment often presents challenges due to the prevalence of normal equipment operation compared to equipment failures. Additionally, the correlations between multiple sensors are established based on production experience and surface-level connections, not fully encompassing the connections between data. Consequently, such data samples often suffer from issues of insufficient labeling. The principles of diffusion can offer significant advantages in such data environments. Moreover, existing GNN methods typically only utilize the direct neighbors of each node, assigning them equal weights during information aggregation [11]. However, the connections between entities are not always direct, and the underlying features behind the graph are often more complex. The constructed graphs often only capture partial information. Applying diffusion models in anomaly detection maximizes the value of limited data, enhances the compatibility of the model across varying data qualities, and plays a crucial role in the widespread deployment of models in practical production scenarios. In the foreseeable future, diffusion models are expected to frequently emerge in anomaly detection across various industrial domains, particularly in fields where the quality of raw data is not high, making it a primary stronghold for the application of diffusion models. The fundamental concept underlying the graph diffusion model is that it facilitates the smoothing of neighborhoods on the graph, serving as a denoising filter akin to a Gaussian filter applied to an image. This facilitates the model in acquiring vital information pertaining to the graph, as features and edges in graphs within real-world environments tend to exhibit inherent noise. Prior research has similarly underscored the robustness of this concept [37; 38; 39]. The studies affirm that graph diffusion-based smoothing can effectively extract valuable neighborhood information from noisy graphs. Graph diffusion is performed as follows.