Large Scale Dynamic

Large-scale dynamic networks, characterized by constantly evolving structures and relationships, are a central focus of current research, aiming to develop efficient and accurate methods for representation learning and analysis. This involves developing novel graph neural network architectures, such as those employing incremental computation or tensor decomposition, to handle the continuous changes inherent in these systems, often focusing on improving scalability and reducing computational overhead. These advancements are crucial for various applications, including recommendation systems, social network analysis, and biological network modeling, where real-time processing of massive, dynamic data is essential.