GRAPH CONVOLUTIONAL NETWORKS FROM THE PER-SPECTIVE OF SHEAVES AND THE NEURAL TANGENT KERNEL

Graph convolutional networks are a popular class of deep neural network algorithms which have shown success in a number of relational learning tasks. Despite their success, graph convolutional networks exhibit a number of peculiar features, including a bias towards learning oversmoothed and homophilic functions, which are not easily diagnosed due to the complex nature of these algorithms. We propose to bridge this gap in understanding by studying the neural tangent kernel of sheaf convolutional networks–a topological generalization of graph convolutional networks.