  We present diffusion-convolutional neural networks (DCNNs), a new model for
graph-structured data. Through the introduction of a diffusion-convolution
operation, we show how diffusion-based representations can be learned from
graph-structured data and used as an effective basis for node classification.
DCNNs have several attractive qualities, including a latent representation for
graphical data that is invariant under isomorphism, as well as polynomial-time
prediction and learning that can be represented as tensor operations and
efficiently implemented on the GPU. Through several experiments with real
structured datasets, we demonstrate that DCNNs are able to outperform
probabilistic relational models and kernel-on-graph methods at relational node
classification tasks.
