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.
@article{arxiv.1711.05697,
title = {Motif-based Convolutional Neural Network on Graphs},
author = {Aravind Sankar and Xinyang Zhang and Kevin Chen-Chuan Chang},
journal= {arXiv preprint arXiv:1711.05697},
year = {2019}
}