Simplicial Convolutional Neural Network

Simplicial convolutional neural networks (SCNNs) extend traditional graph neural networks by processing data residing not just on nodes, but also on higher-order structures like edges and triangles within simplicial complexes. Current research focuses on developing efficient SCNN architectures, such as binarized versions, and exploring effective pooling strategies to manage computational complexity while preserving crucial topological information. These advancements enable SCNNs to analyze complex, higher-order relationships in data, finding applications in diverse fields like network analysis, trajectory prediction, and neural spike decoding, where capturing higher-order interactions is crucial for accurate modeling.