OCGATL: One-Class Graph Attention Networks with Transformation Learning for Anomaly Detection for Argo Data

As the typical representative of marine big data, the Argo plan conducts high-quality and scientific anomaly detection on Argo data, which is an important step in ocean science big data. However, in classical anomaly algorithms, Argo anomaly detection mostly has low accuracy, poor efficiency, and neglects the spatial continuity of Argo data. In the research on anomaly detection of spatial and regional data, graph anomaly detection has achieved excellent results. In the research of graph anomaly detection, depth based classification as a downstream anomaly detection method performs well, but at the same time, there are also problems of hyper sphere collapse and performance flipping. This article focuses on the research work related to the above issues: (1) Based on the study of Argo data and graph data, combined with the three-dimensional spatial characteristics of Argo buoy data, a novel graph data construction method is proposed. (2) Propose to incorporate neural transformation learning into the architecture, improve data learning expression ability, and further improve the shortcomings of graph neural classification, enabling it to adapt to the spatiotemporal and multi-dimensional characteristics of Argo buoy data for outlier detection. This article conducts experiments on five simulation datasets to demonstrate that the improved idea outperforms five state-of-the-art graph anomaly detection algorithms in various indicators, successfully improving the problems of hyper sphere collapse and performance flipping, and enhancing the accuracy and robustness of graph anomaly detection; The effectiveness of graph construction was demonstrated by comparing it with classical anomaly algorithms on real Argo sample data.