
A novel simplicial neural network architecture, SCRaWl, that leverages random walks and 1D convolutions to efficiently capture higher-order relationships in simplicial data.


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efficient-learning-from-simplicial-data-using-random-walks-and-1d-convolutions


Efficient Learning from Simplicial Data Using Random Walks and 1D Convolutions


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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.


generalized-simplicial-attention-neural-networks-a-novel-architecture-for-processing-data-on-simplicial-complexes


Generalized Simplicial Attention Neural Networks: A Novel Architecture for Processing Data on Simplicial Complexes
