  Following the success of deep convolutional networks in various vision and
speech related tasks, researchers have started investigating generalizations of
the well-known technique for graph-structured data. A recently-proposed method
called Graph Convolutional Networks has been able to achieve state-of-the-art
results in the task of node classification. However, since the proposed method
relies on localized first-order approximations of spectral graph convolutions,
it is unable to capture higher-order interactions between nodes in the graph.
In this work, we propose a motif-based graph attention model, called Motif
Convolutional Networks (MCNs), which generalizes past approaches by using
weighted multi-hop motif adjacency matrices to capture higher-order
neighborhoods. A novel attention mechanism is used to allow each individual
node to select the most relevant neighborhood to apply its filter. Experiments
show that our proposed method is able to achieve state-of-the-art results on
the semi-supervised node classification task.
