Adaptive Graph Diffusion Networks

Understand

Graph Neural Networks (GNNs) have received much attention in the graph deep learning domain.

• However, recent research empirically and theoretically shows that deep GNNs suffer from over-fitting and over-smoothing problems.
• The usual solutions either cannot solve extensive runtime of deep GNNs or restrict graph convolution in the same feature space.
• We propose the Adaptive Graph Diffusion Networks (AGDNs) which perform multi-layer generalized graph diffusion in different feature spaces with moderate complexity and runtime.