Graph Model

Graph models represent data as networks of interconnected nodes and edges, aiming to capture relationships and patterns within complex datasets. Current research focuses on developing more robust and efficient graph models, including Graph Neural Networks (GNNs) and their integration with Large Language Models (LLMs), addressing challenges like backdoor attacks, dynamic graph evolution, and cross-domain generalization. These advancements are significant for various applications, improving performance in areas such as recommendation systems, drug discovery, and autonomous driving, while also enhancing the interpretability and fairness of graph-based machine learning.