Graph Transformation

Graph transformation focuses on modifying graph structures, aiming to optimize properties or infer underlying rules governing graph dynamics. Current research emphasizes automated rule inference, leveraging techniques like set cover algorithms and graph neural networks (GNNs), as well as developing novel architectures such as graph transformers and hypernetworks for enhanced representation learning and prediction in diverse applications. These advancements are improving graph-based modeling across various fields, including chemistry, malware detection, and image processing, by enabling more efficient and accurate analysis of complex relational data.