  In recent years, there has been a surge of interest in developing deep
learning methods for non-Euclidean structured data such as graphs. In this
paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture
that alternates convolution-like operations on the graph and its dual. Our
approach allows to learn both vertex- and edge features and generalizes the
previous graph attention (GAT) model. We provide extensive experimental
validation showing state-of-the-art results on a variety of tasks tested on
established graph benchmarks, including CORA and Citeseer citation networks as
well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender
systems.
