  We present a novel graph diffusion-embedding networks (GDEN) for graph
structured data. GDEN is motivated by our closed-form formulation on
regularized feature diffusion on graph. GDEN integrates both regularized
feature diffusion and low-dimensional embedding simultaneously in a unified
network model. Moreover, based on GDEN, we can naturally deal with structured
data with multiple graph structures. Experiments on semi-supervised learning
tasks on several benchmark datasets demonstrate the better performance of the
proposed GDEN when comparing with the traditional GCN models.
