Network Model

Network models are mathematical representations of interconnected systems, aiming to understand and predict their behavior. Current research focuses on developing sophisticated models using graph neural networks (GNNs), stochastic block models (SBMs), and agent-based approaches, often incorporating techniques like Fisher Information Matrix-based unlearning and deep Gaussian process emulation to improve accuracy and efficiency. These advancements are crucial for diverse applications, including social network analysis, disease modeling, network optimization, and risk prediction, enabling more accurate insights and informed decision-making across various fields.