MotifNet: a motif-based Graph Convolutional Network for directed graphs

Understand

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.