Graph Stream

Graph stream processing focuses on efficiently analyzing and learning from continuously evolving graph data, aiming to extract meaningful patterns and make timely predictions. Current research emphasizes developing scalable algorithms and model architectures, such as dynamic graph neural networks (GNNs) and incremental learning methods, to handle the challenges of real-time updates and concept drift in streaming graphs. These advancements are crucial for applications requiring real-time analysis of dynamic systems, including social networks, cyber-physical systems, and recommendation systems, enabling more effective anomaly detection, predictive modeling, and resource optimization.