Deep Graph Neural Networks for Detecting Anomalies in Large-Scale Data Streams

It is essential to promptly identify anomalies in big data streams, unlike in the past, when data was streamed slowly and contained small volumes of information for applications such as cybersecurity, IoT devices, and fraud detection. The classical methods are ill-suited for identifying the real-time patterns of data streams, which are frequently complex, high-dimensional, and related. However, the problem can be solved efficiently with Deep Graph Neural Networks (DGNNs) as the former can identify complicated relations and dynamic patterns While operating based on the graph structures in big data. Furthermore, this paper aims to analyze the application of DGNNs for anomaly detection in big data streams, studying the scalability of approaches, real-time processing, and dynamic graph adjustment methods. Through overcoming issues like computational load, model, interpretability, and the concept of drift, the present work represents how DGNNs improve Anomaly detection precision. Real-world application scenarios in cybersecurity, IoT, and financial fraud, combined with DGNN-based frameworks, prove the effectiveness of the outlined ideas. Lastly, we present recent developments and prospects, including edge computing and reinforcement learning, to get a fully autonomous online anomaly detection framework. Keywords: Big data streams, Spatio-temporal networks, Concept drift, Cybersecurity, Edge features, Internet of Things (IoT), Machine learning models, Graph embeddings, Anomaly detection, Deep Graph Neural Networks (DGNNs), Temporal patterns, Distributed computing, Graph-based algorithms, Node embeddings, Financial fraud detection, Real-time processing, Scalability, Streaming analytics, Dynamic graphs.