A novel simplicial neural network architecture, SCRaWl, that leverages random walks and 1D convolutions to efficiently capture higher-order relationships in simplicial data.
This paper introduces GSAN, a novel neural network architecture specifically designed to process data structured on simplicial complexes, leveraging the power of masked self-attention mechanisms and principles of topological signal processing for enhanced learning and representation capabilities.