Local Global Interactive Graph

Local-global interactive graphs represent a powerful approach to graph-based machine learning, aiming to leverage both fine-grained local information and broader global context within a single model. Current research focuses on developing novel architectures, such as graph attention networks and federated learning frameworks, that effectively integrate these different levels of information, often addressing challenges like heterophily and data distribution across multiple clients. These advancements improve performance in tasks such as node classification, graph matching, and sentiment analysis, demonstrating the significant impact of incorporating both local and global graph structure for enhanced accuracy and efficiency.