Simplicial Convolutional

Simplicial convolutional neural networks (SCNNs) extend 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 improving SCNN efficiency, for example through binarization strategies and optimized pooling methods, as well as developing novel architectures like simplicial attention networks and autoencoders for tasks such as semantic communication and inference. This approach offers advantages in handling complex, high-dimensional data, leading to improved accuracy and efficiency in applications ranging from analyzing network data to image classification and beyond.