  Deep learning on graphs and in particular, graph convolutional neural
networks, have recently attracted significant attention in the machine learning
community. Many of such techniques explore the analogy between the graph
Laplacian eigenvectors and the classical Fourier basis, allowing to formulate
the convolution as a multiplication in the spectral domain. One of the key
drawback of spectral CNNs is their explicit assumption of an undirected graph,
leading to a symmetric Laplacian matrix with orthogonal eigendecomposition. In
this work we propose MotifNet, a graph CNN capable of dealing with directed
graphs by exploiting local graph motifs. We present experimental evidence
showing the advantage of our approach on real data.
