HyPe GT

HyPe-GT is a framework enhancing Graph Transformers (GTs) by incorporating hyperbolic positional encodings to improve the processing of graph-structured data. Current research focuses on optimizing these encodings for various downstream tasks and mitigating over-smoothing in deep Graph Neural Networks, often using hyperbolic neural networks or hyperbolic graph convolutional networks. This approach aims to address limitations in existing GTs by incorporating positional information, leading to more accurate and efficient processing of complex relationships within graph data. The impact extends to diverse fields, including molecular modeling and network analysis, where improved graph representation learning can significantly advance scientific understanding and applications.