  Many real-world problems can be represented as graph-based learning problems.
In this paper, we propose a novel framework for learning spatial and
attentional convolution neural networks on arbitrary graphs. Different from
previous convolutional neural networks on graphs, we first design a
motif-matching guided subgraph normalization method to capture neighborhood
information. Then we implement subgraph-level self-attentional layers to learn
different importances from different subgraphs to solve graph classification
problems. Analogous to image-based attentional convolution networks that
operate on locally connected and weighted regions of the input, we also extend
graph normalization from one-dimensional node sequence to two-dimensional node
grid by leveraging motif-matching, and design self-attentional layers without
requiring any kinds of cost depending on prior knowledge of the graph
structure. Our results on both bioinformatics and social network datasets show
that we can significantly improve graph classification benchmarks over
traditional graph kernel and existing deep models.
