Graph neural networks based on geometric algebra
Background Geometric Algebra (GA) provides a unified mathematical framework for representing and manipulating geometric entities and transformations in arbitrary dimensions. Its ability to elegantly encode rotations, reflections, and other symmetries makes it an ideal tool for advancing geometric deep learning. While current Graph Neural Networks (GNNs) have made significant strides in processing molecular data, they often rely on specialized techniques to handle equivariance and fail to fully leverage the expressive power of GA. ...