Message Passing Network

Message Passing Networks (MPNs) are a class of graph neural networks designed to process information across interconnected nodes by iteratively exchanging "messages" representing node features. Current research focuses on improving MPN efficiency and scalability for large graphs, developing adaptive and dynamic architectures (e.g., those with learned hierarchies or recurrent message passing), and enhancing their expressive power for tasks like semantic segmentation, physics simulation, and knowledge graph completion. These advancements are driving progress in diverse fields, including drug discovery, geospatial analysis, and multi-agent systems, by enabling more accurate and efficient processing of complex relational data.