graph convolution is the core of most graph neural networks (GNNs) and
usually approximated by message passing between direct (one-hop) neighbors. In
this work, we remove the restriction of using only the direct
GND-Nets, a new graph neural network that exploits local and global neighborhood information, is proposed to mitigate the over-smoothing and under-smoothing problems of Graph Convolutional Networks, using a new graph diffusion method called neural diffusions, which integrate neural networks into the conventional linear and nonlinear graph diffusions.