  This paper introduces a generalization of Convolutional Neural Networks
(CNNs) to graphs with irregular linkage structures, especially heterogeneous
graphs with typed nodes and schemas. We propose a novel spatial convolution
operation to model the key properties of local connectivity and translation
invariance, using high-order connection patterns or motifs. We develop a novel
deep architecture Motif-CNN that employs an attention model to combine the
features extracted from multiple patterns, thus effectively capturing
high-order structural and feature information. Our experiments on
semi-supervised node classification on real-world social networks and multiple
representative heterogeneous graph datasets indicate significant gains of 6-21%
over existing graph CNNs and other state-of-the-art techniques.

Machine Learning;Social and Information Networks