The purpose of this paper is to elucidate the theory and mathematical modelling behind the sheaf neural network (SNN) algorithm and then show how SNN can effectively answer to biomedical questions in a concrete case study and outperform the most popular graph neural networks (GNNs) as graph convolutional networks (GCNs), graph attention networks (GAT) and GraphSage.

翻译：本文旨在阐明层状神经网络（SNN）算法背后的理论与数学模型，并通过具体案例展示SNN如何有效回答生物医学问题，同时在性能上超越最流行的图神经网络（GNN），如图卷积网络（GCN）、图注意力网络（GAT）和GraphSage。